Deep neural networks (DNNs) have achieved extensive progress in time series learning. However, they could suffer from performance degradation when it comes to label noise in the real world. Towards this end, this paper studies an underexplored yet realistic problem of noise-resistant time series learning and proposes a novel data-centric approach named Dual Data-centric Separation with Circular Mixup (DREAM) for this problem. The core of our DREAM is to explore and exploit the noisy data from dual data-centric views for reduced overfitting. On the one hand, we assume that samples with similar features share similar labels and infer the pseudo label of each sample using its affinity graph to capture the corresponding pseudo margin. On the other hand, we monitor the optimization status by simulating the mislabeled data to generate flexible criteria for accurate separation of clean and noisy samples. In addition, we leverage circular Mixup to interpolate between clean and noisy samples in the embedding space. These mixed samples are incorporated into a discrepancy-aware consistency learning framework to ensure robust time series representations of all the separated samples. Experimental results on a wide range of publicly accessible datasets reveal the effectiveness of our DREAM.

This paper investigates the task of unsupervised graph domain adaptation, which facilitates the transfer of knowledge from labeled source graphs to unlabeled target graphs. Recent approaches usually utilize graph contrastive learning and pseudo-labeling to learn from unlabeled target data, which could introduce potential biased representations and supervision of target graphs resulting from serious shifts across two domains. Towards this end, we propose a novel framework named Diffusion-guided Unbiased Discriminative Learning (DisCo) for unsupervised graph domain adaptation. The core of our DisCo is to leverage both feature disentanglement and cross-domain diffusion signals to remove the potential biases for target graphs. In particular, we first utilize adversarial feature disentanglement to extract causal features that are orthogonal to domain biases. More importantly, we retrieve the labels of cross-domain source graphs to generate the conditions, which would be utilized to optimize a diffusion model for label denoising. The consistency between pseudo-labels and denoised labels is measured to reduce the potential biases during domain alignment. Extensive experiments on several real-world benchmarks demonstrate that our proposed DisCo consistently outperforms competing state-of-the-art baselines.

Graph neural networks (GNNs) have essentially taken over as the de facto model for learning graph-structured data. However, the majority of existing methods perform transductive learning in a known graph, which is unable to tackle abundant in-the-wild unseen graphs with potential domain shifts. Even worse, these graphs, accompanied by domain shifts on structural topology and node attributes, bring in vulnerable data bias and thus a huge drop in performance. To tackle this, we propose a novel GNN method named spectral optimal transport (SPOT) for effective domain generalization on graphs. Our method is motivated by the fact that the high-frequency graph spectrum is more likely to indicate domain differences. In particular, we formulate the structural augmentation as an optimal transport problem to retain low-frequency key knowledge and solve the problem using Sinkhorn-Knopp algorithm. In addition, we incorporate an adaptive perturbation strategy to deep features, where the direction of the additive noise is determined by the homophily degrees to maintain semantic properties. Accordingly, we meticulously construct a collection of real-world benchmark datasets to assess the domain generalization capability of our model on graphs, and extensive experiments confirm the effectiveness of our proposed SPOT.

Out-of-distribution (OOD) detection has garnered increasing concern for identifying test samples that exhibit a distributional shift from the training dataset in practical deep learning applications. With the significant advancements in graph deep learning for graph representation, graph OOD detection has emerged as a research problem. Graph contrastive learning (GCL) is applied to graph OOD detection due to its capacity for learning discriminative representations in a self-supervised manner, thereby eliminating the need for time-consuming and labor-intensive label information. However, existing methods often neglect the explicit consideration of underlying semantics behind graph data distribution for OOD detection. We argue that simple data augmentations for GCL may risk disrupting the intrinsic graph structure while retaining redundant structural information, which hinders semantic discrimination between graphs. Additionally, Euclidean space embedding struggles to maintain hierarchical structural consistency, making it challenging to meaningfully capture the hierarchical semantic distribution of graph data. In response to these issues, we propose a novel framework termed HGOOD-D, which aims to explore latent semantic hierarchies in hyperbolic space for graph OOD detection. Specifically, we design a bottleneck graph extractor grounded in the information bottleneck (IB) principle, which captures the minimal sufficient information to distinguish graph patterns. Based on this, we introduce hierarchical contrastive learning to capture the hierarchical semantics within graph data distribution. These methods are based on hyperbolic space embedding that can preserve complex inter-relationships in graph hierarchies, thereby mitigating data distortion. Comprehensive evaluations on ten widely used benchmark datasets show that HGOOD-D consistently surpasses current state-of-the-art approaches in graph OOD detection.

Graph domain adaptation has emerged as a critical challenge in real-world applications, where labeled graph data is often scarce and expensive to obtain. While existing methods have shown promise, they typically require access to source domain data, which may be restricted due to privacy concerns or data regulations. To address these limitations, we investigate the challenging yet practical problem of source-free graph domain adaptation. We propose a new approach namedRobust CrossSupervision with Target Mining (ROSE) for this problem. ROSE achieves robustness by considering the complementary topology of graphs. The model consists of a message-passing branch for local semantic learning and a graph-kernel branch for global structural capture. Both branches are incorporated into a unified cross-supervision framework. To improve the robustness of the optimization process, we explore the context of the target domain, and divide the target data into discriminant set and anchor set. Then we incorporate the two tasks into a meta-learning optimization framework. Extensive experiments on benchmark datasets have demonstrated that our ROSE, compared with a wide range of baselines, always yields superior performance. The source code is available athttps://github.com/luo-junyu/ROSE.

Graph retrieval (GR), a ranking procedure that aims to sort the graphs in a database by their relevance to a query graph in decreasing order, has wide applications across diverse domains, such as visual object detection and myreddrug discovery. Existing Graph Retrieval (GR) approaches usually compare graph pairs at a detailed level and generate quadratic similarity scores. In realistic scenarios, conducting quadratic fine-grained comparisons is costly. However, coarse-grained comparisons would result in performance loss. Moreover, label scarcity in real-world data brings extra challenges. To tackle these issues, we investigate a more realistic GR problem, namely, efficient graph retrieval (EGR). Our key intuition is that, since there are numerous underutilized unlabeled pairs in realistic scenarios, by leveraging the additional information they provide, we can achieve speed-up while simplifying the model without sacrificing performance. Following our intuition, we propose an efficient model called Dual-TowerModel withDividing, Contrasting andAlignment (TowerDNA). TowerDNA utilizes a GNN-based dual-tower model as a backbone to quickly compare graph pairs in a coarse-grained manner. In addition, to effectively utilize unlabeled pairs, TowerDNA first identifies confident pairs from unlabeled pairs to expand labeled datasets. It then learns from remaining unconfident pairs via graph contrastive learning with geometric correspondence. To integrate all semantics with reduced biases, TowerDNA generates prototypes using labeled pairs, which are aligned within both confident and unconfident pairs. Extensive experiments on diverse realistic datasets demonstrate that TowerDNA achieves comparable performance to fine-grained methods while providing a 10× speed-up.

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework namedCriterion-guidedHeterogeneousCollaborativeFiltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.

Graph neural networks (GNNs) have achieved great success recently on graph classification tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels could exist in the real world because of the complicated processes of manual graph data annotations, which may significantly degrade the performance of GNNs. Therefore, we investigate the problem of graph classification with label noise, which is demanding because of the complex graph representation learning issue and serious memorization of noisy samples. In this work, we present a novel approach called S ubgra p h Set Netw or k with Sample Selection and Consis t ency Learning (SPORT) for this problem. To release the overfitting of GNNs, SPORT proposes to characterize each graph as a set of subgraphs generated by certain predefined stratagems, which can be viewed as samples from its underlying semantic distribution in graph space. Then we develop an equivariant network to encode the subgraph set with the consideration of the symmetry group. To further release the influences of noisy examples, we leverage the predictions of subgraphs to measure the likelihood of a sample being clean or noisy, followed by effective label updating. In addition, we propose a joint loss to advance the model generalizability by introducing consistency regularization. Comprehensive experiments on a wide range of graph classification datasets demonstrate the effectiveness of our SPORT. Specifically, SPORT outperforms the most competing baseline by up to 6.4%.

Due to the excellent computing efficiency, learning to hash has acquired broad popularity for Big Data retrieval. Although supervised hashing methods have achieved promising performance recently, they presume that all training samples are appropriately annotated. Unfortunately, label noise is ubiquitous owing to erroneous annotations in real-world applications, which could seriously deteriorate the retrieval performance due to imprecise supervised guidance and severe memorization of noisy data. Here we propose a comprehensive method DIOR to handle the difficulties of learning to hash with label noise. DIOR performs partitions from two complementary levels, namely sample level and parameter level. On the one hand, DIOR divides the dataset into a labeled set with clean samples and an unlabeled set with noisy samples using an ensemble of perturbed views. Then we train the network in a contrastive semi-supervised manner by reconstructing label embeddings for both reliable supervision of clean data and sufficient exploration of noisy data. On the other hand, inspired by recent pruning techniques, DIOR divides the parameters in the hashing network into crucial parameters and non-crucial parameters, and then optimizes them separately to reduce the overfitting of noisy data. Extensive experiments on four popular benchmark datasets demonstrate the effectiveness of DIOR.

Hashing aims to compress raw data into compact binary descriptors, which has drawn increasing interest for efficient large-scale image retrieval. Current deep hashing often employs evaluation protocols where usually query data and training data are from similar distributions. However, more realistic evaluations should take into account a broad spectrum of distribution shifts with varying degrees. Therefore, we study the problem of out-of-distribution generalization in image retrieval, which seeks to learn a retrieval model from a source domain and generalize to unseen target domains. However, this problem is challenging owing to data scarcity in target domains and the potential overfitting of domain-specific patterns. Here, we propose a novel hashing model namedLooking-into-gradients (LOG) for image retrieval under out-of-distribution shifts, which comprehensively explores gradients for both data generation and model optimization. Specifically, to overcome data deficiency in target domains, we formalize the worst-case problem to generate challenging virtue samples via adversarial gradient ascend. Besides, to further enhance model generalization capability, we not only identify non-crucial parameters with minor gradients and values and shrink them to zero, but also modify the inconsistent gradients across domains to prevent learning domain-specific patterns. Extensive experiments on various datasets demonstrate that LOG outperforms state-of-the-art methods by up to 8.54%.

This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity issue of supervised information on node classification. While promising results are achieved, existing works for this problem usually suffer from the poor balance of generalization and fitting ability due to the heavy reliance on labels or task-agnostic unsupervised information. To address the challenge, we propose a dual-channel framework for semi-supervised learning on G raphs via K nowledge T ransfer between independent supervised and unsupervised embedding spaces, namely, GKT. Specifically, we devise a dual-channel framework including a supervised model for learning the label probability of nodes and an unsupervised model for extracting information from massive unlabeled graph data. A knowledge transfer head is proposed to bridge the gap between the generalization and fitting capability of the two models. We use the unsupervised information to reconstruct batch-graphs to smooth the label probability distribution on the graphs to improve the generalization of prediction. We also adaptively adjust the reconstructed graphs by encouraging the label-related connections to solidify the fitting ability. Since the optimization of the supervised channel with knowledge transfer contains that of the unsupervised channel as a constraint and vice versa, we then propose a meta-learning-based method to solve the bi-level optimization problem, which avoids the negative transfer and further improves the model’s performance. Finally, extensive experiments validate the effectiveness of our proposed framework by comparing state-of-the-art algorithms.

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed D eep A daptive G raph C lustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρ k which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., 𝒫( H | ρ ) and 𝒫( ρ | H ), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets.

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations in traffic data using spatio-temporal graph neural networks (GNNs). However, the performance of these methods is still far from satisfactory since GNNs usually have limited representation capacity when it comes to complex traffic networks. Graphs, by nature, fall short in capturing non-pairwise relations. Even worse, existing methods follow the paradigm of message passing that aggregates neighborhood information linearly, which fails to capture complicated spatio-temporal high-order interactions. To tackle these issues, in this paper, we propose a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise relationships, our DyHSL extracts hypergraph structural information to model dynamics in the traffic networks, and updates each node representation by aggregating messages from its associated hyperedges. Additionally, to capture high-order spatio-temporal relations in the road network, we introduce an interactive graph convolution block, which further models the neighborhood interaction for each node. Finally, we integrate these two views into a holistic multi-scale correlation extraction module, which conducts temporal pooling with different scales to model different temporal patterns. Extensive experiments on four popular traffic benchmark datasets demonstrate the effectiveness of our proposed DyHSL compared with a broad range of competing baselines.

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing model from a label-rich source domain to a label-scarce target domain. Current state-of-the-art approaches generally resolve the problem by integrating pseudo-labeling and domain adaptation techniques into deep hashing paradigms. Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. The core of our DANCE is to perform contrastive learning on hash codes from both instance level and prototype level. To begin, DANCE utilizes label information to guide instance-level hashing contrastive learning in the source domain. To generate unbiased and reliable pseudo-labels for semantic learning in the target domain, we uniformly select samples around each label embedding in the Hamming space. A momentum-update scheme is also utilized to smooth the optimization process. Additionally, we measure the semantic prototype representations in both source and target domains and incorporate them into a domain-aware prototype-level contrastive learning paradigm, which enhances domain alignment in the Hamming space while maximizing the model capacity. Experimental results on a number of well-known domain adaptive retrieval benchmarks validate the effectiveness of our proposed DANCE compared to a variety of competing baselines in different settings.

Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods, and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing, and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion.

Urban traffic light control is an important and challenging real-world problem. By regarding intersections as agents, most of the reinforcement learning-based methods generate agents’ actions independently. They can cause action conflict and result in overflow or road resource waste in adjacent intersections. Recently, some collaborative methods have alleviated the above problems by extending the observable surroundings of agents, which can be considered inactive cross-agent communication methods. However, when agents act synchronously in these works, the perceived action value is biased, and the information exchanged is insufficient. In this work, we first propose a novel Multi-agent Communication and Action Rectification (MaCAR) framework. It enables active communication between agents by considering the impact of synchronous actions of agents. Another fundamental problem of traffic light control is the balance between traffic demand and road supply capacity. To fully describe the relation between traffic demand and road supply capacity (Supply-Demand modeling, SD), we further model and forecast the Supply-Demand relation to facilitating the effectiveness of the model’s action. The experiments show that our model outperforms state-of-the-art methods on both synthetic and real-world datasets. Combining the SD with MaCAR, SD-MaCAR can further boost the traffic light control performance even in traffic accident scenarios.

In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. The problem is typically solved by learning graph neural networks with pseudo-labeling or knowledge distillation to incorporate both labeled and unlabeled graphs. However, these methods usually either suffer from overconfident and biased pseudo-labels or suboptimal distillation caused by the insufficient use of unlabeled data. Inspired by the recent progress of contrastive learning and dual learning, we propose DualGraph, a principled framework to leverage unlabeled graphs more effectively for semi-supervised graph classification. DualGraph consists of a prediction module and a retrieval module to model graphs$G$and their labels$y$from opposite while complementary views (i.e., p(y | G) and p(G | y) respectively). The two modules are jointly trained via posterior regularization, which encourages their inter-module consistency on unlabeled graphs. Moreover, we improve model training for each module with a contrastive learning framework to encourage the intra-module consistency on unlabeled data. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DualGraph.

Hypergraph Convolutional Network (HCN) has be-come a proper choice for capturing high-order relationships. Existing HCN methods are tailored for static hypergraphs, which are unsuitable for the dynamic evolution in real-world scenarios. In this paper, we explore a dynamic HCN based on the attention mechanism (DyHCN) for time series prediction. It not only effectively exploits the spatial and temporal relationships in the dynamic hypergraph, but also continuously aggregates the temporal evolution cues of time-varying hypergraphs with the global and local embeddings. Specifically, these merits can be attributed to 1) dynamic hypergraph construction (DHC), which captures the feature of historical context content and provides a guideline for dynamic hypergraph construction; 2) spatio-temporal hypergraph convolution module (STHC), responsible for extracting the spatial and temporal relationships among nodes and hyperedges, and 3) collaborative prediction module (CP), for the overall time-varying hypergraphs embedding aggregation. Such modules endeavor to well learn feature embedding from nodes, hyperedges, and hypergraphs, which produces informative representations for downstream tasks. Experiments on three datasets including Tiingo, Stocktwits, and NYC-Taxi demonstrate that the proposed DyHCN achieves sound performance over existing cousins, and both STHC and CP modules play a key role in modeling the dynamic evolution property of hypergraphs.

A city is an aggregate of a huge amount of heterogeneous data. However, extracting meaningful values from that data remains challenging. City Brain is an end-to-end system whose goal is to glean irreplaceable values from big city data, specifically from videos, with the assistance of rapidly evolving AI technologies and fast-growing computing capacity. From cognition to optimization, to decision-making, from search to prediction and ultimately, to intervention, City Brain improves the way we manage the city, as well as the way we live in it. In this talk, firstly we will introduce current practices of the City Brain platform in a few cities in China, including what we can do to achieve the goal and make it a reality. Then we will focus on visual search technologies and applications that we can apply on the city data. Last, a few video demos will be shown, followed by highlighting a few future directions of city computing.

Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, friend recommendation is a difficult task and most social websites use simple friend recommendation algorithms such as similarity and popularity, whose level of accuracy does do not satisfy the majority of users. In this paper we propose a two-stage procedure for more accurate friend recommendation: In the first stage, based on the relationship of different social networks, the Flickr tag network and contact network are aligned to generate a "possible friend list"; In the second stage, making the assumption that "a friend's friends also tend to be friends", co-clustering is applied to the tag and image information of the list to refine the recommendation result in the first stage. Experimental results show that the proposed method achieves good performance and every stage contributes to the recommendation.

The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank true matrix and a sparse error matrix. We then formulate an optimization problem by regularizing the true matrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the true community indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.

No abstract available.

With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time consuming for training models. In this paper, we address these difficulties by proposing a regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale data sets crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking adaptability measurement.

In this paper, we investigate how to design an optimized discriminating order for boosting multiclass classification. The main idea is to optimize a binary tree architecture, referred to as Sequential Discriminating Tree (SDT), that performs the multiclass classification through a hierarchical sequence of coarse-to-fine binary classifiers. To infer such a tree architecture, we employ the constrained large margin clustering procedure which enforces samples belonging to the same class to locate at the same side of the hyper plane while maximizing the margin between these two partitioned class subsets. The proposed SDT algorithm has a theoretic error bound which is shown experimentally to effectively guarantee the generalization performance. Experiment results indicate that SDT clearly beats the state-of-the-art multiclass classification algorithms.

Kernel method is a powerful tool in multi-instance learning. However, many typical kernel methods for multi-instance learning ignore the correspondence information of instances between two bags or co-occurrence information, and result in poor performance. Additionally, most current multi-instance kernels unreasonably assign all instances in each bag an equal weight, which neglects the significance of some “key” instances in multi-instance learning. Last but not least, almost all the multi-instance kernels encounter a heavy computation load, which may fail in large datasets. To cope with these shortcomings, we propose a FAst kernel for Multi-instancE leaRning named as FAMER. FAMER constructs a Locally Sensitive Hashing (LSH) based similarity measure for multi-instance framework, and represents each bag as a histogram by embedding instances within the bag into an auxiliary space, which captures the correspondence information between two bags. By designing a bin-dependent weighting scheme, we not only impose different weights on instances according to their discriminative powers, but also exploit co-occurrence relations according to the joint statistics of instances. Without directly computing in a pairwise manner, the time complexity of FAMER is much smaller compared to other typical multi-instance kernels. The experiments demonstrate the effectiveness and efficiency of the proposed method.

In this paper, a graph cut based tag enrichment approach is proposed. We build a graph for each image with its initial tags. The graph is with two terminals. Nodes of the graph are full connected with each other. Min-cut/max-flow algorithm is utilized to find the relevant tags for the image. Experiments on Flickr dataset demonstrate the effectiveness of the proposed graph-cut based tag enrichment approach.

No abstract available.

Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction , uncertainty , diversity , density and relevance . We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation.

The availability of large-scale images from the Internet has made the research on image search attract a lot of attention. Text-based image search engines, for example, Google/Microsoft Bing/Yahoo! image search engines using the surrounding text, have been developed and widely used. However, they suffer from an inability to search image content. In this article, we present an interactive image search system, image search by color map, which can be applied to, but not limited to, enhance text-based image search. This system enables users to indicate how the colors are spatially distributed in the desired images, by scribbling a few color strokes, or dragging an image and highlighting a few regions of interest in an intuitive way. In contrast to the conventional sketch-based image retrieval techniques, our system searches images based on colors rather than shapes, and we, technically, propose a simple but effective scheme to mine the latent search intention from the user’s input, and exploit the dominant color filter strategy to make our system more efficient. We integrate our system to existing Web image search engines to demonstrate its superior performance over text-based image search. The user study shows that our system can indeed help users conveniently find desired images.

With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.

Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration.

In this paper, we present a novel image search system, image search by concept map. This system enables users to indicate not only what semantic concepts are expected to appear but also how these concepts are spatially distributed in the desired images. To this end, we propose a new image search interface to enable users to formulate a query, called concept map, by intuitively typing textual queries in a blank canvas to indicate the desired spatial positions of the concepts. In the ranking process, by interpreting each textual concept as a set of representative visual instances, the concept map query is translated into a visual instance map, which is then used to evaluate the relevance of the image in the database. Experimental results demonstrate the effectiveness of the proposed system.

The tags on social media websites such as Flickr are frequently imprecise and incomplete, thus there is still a gap between these tags and the actual content of the images. This paper proposes a social image ``retagging'' scheme that aims at assigning images with better content descriptors. The refining process is formulated as an optimization framework based on the consistency between ``visual similarity'' and ``semantic similarity'' in social images. An effective iterative bound optimization algorithm is applied to learn the optimal tag assignment. In addition, as many tags are intrinsically not closely-related to the visual content of the images, we employ a knowledge-based method to differentiate visual content related from unrelated tags and then constrain the tagging vocabulary of our automatic algorithm within the content related tags. Experimental results on a Flickr image collection demonstrate the effectiveness of this approach.

In this demo, we present a novel interactive image search system, image search by 2D semantic map. This system enables users to indicate what semantic concepts are expected to appear and even how these concepts are spatially distributed in the desired images. To this end, we design an intuitive interface for users to formulate a query in the form of 2D semantic map, called concept map, by typing textual queries in a blank canvas. In the ranking process, by interpreting each textual concept as a set of representative visual instances, the concept map query is translated into a visual instance map, which is then used for comparison with the images in the database. Besides, in this demo, we also show an image search system with a simplest semantic map, a 2D color map, where the concepts are limited from the colors.

This article introduces an intelligent system that accommodates colorblind users in image search. Color plays an important role in the human perception and recognition of images. However, there are about 8% of men and 0.8% of women suffering from colorblindness. We show that the existing image search techniques cannot provide satisfactory results for these users since many images will not be well perceived by them due to the loss of color information. To deal with this difficulty, we introduce a system named Accessible Image Search (AIS) to accommodate these users. Different from the general image search scheme that aims at returning more relevant results, AIS further takes into account the colorblind accessibilities of the returned results, that is, the image qualities in the eyes of colorblind users. The system contains three components: accessibility assessment, accessibility improvement, and color indication. The accessibility assessment component measures the accessibility scores of images, and consequently different reranking methods can be performed to prioritize images with high accessibilities. In the accessibility improvement component, we propose an efficient recoloring algorithm to modify the colors of the images such that they can be better perceived by colorblind users. Color indication aims to indicate the name of the interesting color in an image. We evaluate the introduced system with more than 60 queries and 20 anonymous colorblind users, and the empirical results demonstrate its effectiveness and usefulness.

Recently, various domain-specific search engines emerge, which are restricted to specific topicalities or document formats, and vertical to the broad-based search. Simply applying the ranking model trained for the broad-based search to the verticals cannot achieve a sound performance due to the domain differences, while building different ranking models for each domain is both laborious for labeling sufficient training samples and time-consuming or the training process. In this paper, to address the above difficulties, we investigate two problems: (1) whether we can adapt the ranking model learned for existing Web page search or verticals, to the new domain, so that the amount of labeled data and the training cost is reduced, while the performance requirement is still satisfied; and (2) how to adapt the ranking model from auxiliary domains to a new target domain. We address the second problem from the regularization framework and an algorithm called ranking adaptation SVM is proposed. Our algorithm is flexible enough, which needs only the prediction from the existing ranking model, rather than the internal representation of the model or the data from auxiliary domains. The first problem is addressed by the proposed ranking adaptability measurement, which quantitatively estimates if an existing ranking model can be adapted to the new domain. Extensive experiments are performed over Letor benchmark dataset and two large scale datasets crawled from different domains through a commercial internet search engine, where the ranking model learned for one domain will be adapted to the other. The results demonstrate the applicabilities of the proposed ranking model adaptation algorithm and the ranking adaptability measurement.

This paper presents a new video advertising system, called AdOn, which supports intelligent overlay video ads. Unlike most current ad-networks such as Youtube that overlay the ads at fixed positions in the videos (e.g., on the bottom fifth of videos 15 seconds in), AdOn is able to automatically detect a set of spatio-temporal nonintrusive positions and associate the contextually relevant ads with these positions. The overlay positions are obtained on the basis of video structuring, face and text detection, as well as visual saliency analysis, so that the intrusiveness to the users can be minimized. The ads are selected according to content-based multimodal relevance so that advertising relevance can be maximized. AdOn represents one of the first attempts towards intelligent overlay video advertising by leveraging video content analysis techniques.

Most existing approaches to visual search reranking predominantly focus on mining information within the initial search results. However, the initial ranked list cannot provide enough cues for reranking by itself due to the typically unsatisfying visual search performance. This paper presents a new method for visual search reranking called CrowdReranking, which is characterized by mining relevant visual patterns from image search results of multiple search engines which are available on the Internet. Observing that different search engines might have different data sources for indexing and methods for ranking, it is reasonable to assume that there exist different search results yet certain common visual patterns relevant to a given query among those results. We first construct a set of visual words based on the local image patches collected from multiple image search engines. We then explicitly detect two kinds of visual patterns, i.e., salient and concurrent patterns, among the visual words. Theoretically, we formalize reranking as an optimization problem on the basis of the mined visual patterns and propose a close-form solution. Empirically, we conduct extensive experiments on several real-world search engines and one benchmark dataset, and show that the proposed CrowdReranking is superior to the state-of-the-art works.

There are about 8% of men and 0.8% of women suffering from colorblindness. Due to certain loss of color information, the existing image search techniques may not provide satisfactory results for these users. In this demonstration, we show an image search system that can accommodate colorblind users. It can help these special users find and enjoy what they want by providing multiple services for them, including search results reranking, image recoloring and color indication.

This poster introduces a novel concept-based video indexing approach. It is developed based on a rich set of base concepts, of which the models are available. Then, for a given concept with several labeled samples, we combine the base concepts to fit it and its model can thus be obtained accordingly. Empirical results demonstrate that this method can achieve great performance even with very limited labeled data. We have compared different representation approaches including both sparse and non-sparse methods. Our conclusion is that the sparse method will lead to much better performance.

Learning to rank has become a popular approach to build a ranking model for Web search recently. Based on our observation, the constitution of the training set will greatly influence the performance of the learned ranking model. Meanwhile, the number of queries in Web search is nearly infinite and the human labeling cost is expensive, hence a subset of queries need to be carefully selected for training. In this paper, we develop a greedy algorithm to sample the queries, by simultaneously taking the query density, difficulty and diversity into consideration. The experimental results on a collected Web search dataset comprising 2024 queries show that the proposed method can lead to a more informative training set for building an effective model.

This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. Given a Web page which typically contains images, GameSense is able to select suitable images to create online in-image games for advertising. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games. The ads are selected based on not only textual relevance but also visual content similarity. The game is able to provide viewers rich experience and thus promote the embedded ads to provide more effective advertising.

Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection

Social tagging provides valuable and crucial information for large-scale web image retrieval. It is ontology-free and easy to obtain; however, irrelevant tags frequently appear, and users typically will not tag all semantic objects in the image, which is also called semantic loss. To avoid noises and compensate for the semantic loss, tag recommendation is proposed in literature. However, current recommendation simply ranks the related tags based on the single modality of tag co-occurrence on the whole dataset, which ignores other modalities, such as visual correlation. This paper proposes a multi-modality recommendation based on both tag and visual correlation, and formulates the tag recommendation as a learning problem. Each modality is used to generate a ranking feature, and Rankboost algorithm is applied to learn an optimal combination of these ranking features from different modalities. Experiments on Flickr data demonstrate the effectiveness of this learning-based multi-modality recommendation strategy.

Maximum margin clustering (MMC), which extends the theory of support vector machine to unsupervised learning, has been attracting considerable attention recently. The existing approaches mainly focus on reducing the computational complexity of MMC. The accuracy of these methods, however, has not always been guaranteed. In this paper, we propose to incorporate additional side-information, which is in the form of pairwise constraints, into MMC to further improve its performance. A set of pairwise loss functions are introduced into the clustering objective function which effectively penalize the violation of the given constraints. We show that the resulting optimization problem can be easily solved via constrained concave-convex procedure (CCCP). Moreover, for constrained multi-class MMC, we present an efficient cutting-plane algorithm to solve the sub-problem in each iteration of CCCP. The experiments demonstrate that the pairwise constrained MMC algorithms considerably outperform the unconstrained MMC algorithms and two other clustering algorithms that exploit the same type of side-information.

This paper presents a novel online video recommendation system called VideoReach, which alleviates users' efforts on finding the most relevant videos according to current viewings without a sufficient collection of user profiles as required in traditional recommenders. In this system, video recommendation is formulated as finding a list of relevant videos in terms of multimodal relevance (i.e. textual, visual, and aural relevance) and user click-through. Since different videos have different intra-weights of relevance within an individual modality and inter-weights among different modalities, we adopt relevance feedback to automatically find optimal weights by user click-though, as well as an attention fusion function to fuse multimodal relevance. We use 20 clips as the representative test videos, which are searched by top 10 queries from more than 13k online videos, and report superior performance compared with an existing video site.

Insufficiency of training data is a major obstacle in machine learning and data mining applications. Many different semi-supervised learning algorithms have been proposed to tackle this difficulty by leveraging a large amount of unlabeled data. However, most of them focus on semi-supervised classification. In this paper we propose a semi-supervised regression algorithm named semi-supervised kernel regression (SSKR). While classical kernel regression is only based on labeled examples, our approach extends it to all observed examples using a weighting factor to modulate the effect of unlabeled examples. Experimental results prove that SSKR significantly outperforms traditional kernel regression and graph-based semi-supervised regression methods.

We propose an objective, comprehensive and difficulty-independent performance evaluation protocol for video text detection algorithms. The protocol includes a positive set and a negative set of indices at textbox level, which evaluate the detection quality in terms of both location accuracy and fragmentation of the detected textboxes. In the protocol, we assign a detection difficulty (DD) level to each ground truth textbox. The performance indices can then be normalized with respect to the textbox DD level and are therefore independent of the ground truth difficulty. We also assign a detection importance (DI) level to each ground truth textbox. The overall detection rate is the DI-weighted average of the detection qualities of all ground truth textboxes, which makes the detection rate more accurate to reveal the real performance. The automatic performance evaluation scheme has been applied on a text detection approach to determine the best parameters that can yield the best detection results.