Attribute-Specific Fashion Retrieval (ASFR) focuses on retrieving images based on fine-grained, attribute-specific criteria rather than naive global visual similarity, enabling more precise and interpretable search results. Existing ASFR methods ideally assume that all attribute semantics are in-domain distributions of the training datasets. However, realistic scenarios are generally more complex and naturally contain unseen attribute information, often resulting in ungeneralizable retrieval outcomes. In this paper, we take the first step to address the new and challenging open-world ASFR setting, which involves handling diverse and practical attributes instead of relying solely on predefined attribute sets in closed-world scenarios. Specifically, to comprehend unseen attributes, we propose a novel LLM-based Commonsense Knowledge Infusion (CoKi) framework that integrates commonsense knowledge as complementary context into attribute representations using a Large Language Model (LLM). By infusing such LLM-based commonsense knowledge through descriptive contexts, our method enables robust semantic enrichment and effective generalization to unseen attributes. Additionally, we introduce a modality-switchable prompt and an imputation mechanism to ensure model robustness across diverse input configurations by dynamically adapting to missing modalities. Extensive experiments demonstrate that our approach not only achieves state-of-the-art in-domain retrieval performance but also significantly enhances adaptability to unseen attributes and cross-domain generalization, establishing a new benchmark for fine-grained fashion retrieval in open-world scenarios. Our source code is publicly available at https://github.com/HuiGuanLab/CoKi.

Ship Re-Identification (ReID) aims to accurately identify ships with the same identity across different times and camera views, playing a crucial role in intelligent waterway transportation. However, compared to the widely researched pedestrian and vehicle ReID, Ship ReID has received much less attention, primarily due to the scarcity of large-scale and high-quality ship ReID datasets available for public access. Moreover, several unique challenges make ship ReID particularly difficult: ships are large objects that are hard to capture fully, and the visible area of ships vary significantly due to changes in cargo loading or water surface conditions. These challenges make it difficult to achieve ideal results by directly applying existing ReID methods. To address these challenges, in this paper, we introduce ShipReID-2400, a dataset for ship ReID compiled from a real-world intelligent waterway traffic monitoring system. It comprises 17,241 images of 2,400 distinct ship identities collected over 53 months, ensuring diversity and representativeness. Furthermore, we propose the Disentangle-to-Interact Network ( D2InterNet ), a simple but strong baseline for ship ReID designed to extract discriminative local features despite significant scale variations. Extensive experimental results show that D2InterNet achieves state-of-the-art performance on both the ShipReID-2400 and VesselReID datasets. In addition, despite being designed for ship ReID, D2InterNet also achieves competitive results on the MSMT17 pedestrian ReID dataset, showcasing its good generalization capability. Our dataset and code are publicly available at https://github.com/HuiGuanLab/ShipReID-2400.

Cross-lingual cross-modal retrieval aims at leveraging human-labeled annotations in a source language to construct cross-modal retrieval models for a new target language, due to the lack of manually-annotated dataset in low-resource languages (target languages). Contrary to the growing developments in the field of monolingual cross-modal retrieval, there has been less research focusing on cross-modal retrieval in the cross-lingual scenario. A straightforward method to obtain target-language labeled data is translating source-language datasets utilizing Machine Translations (MT). However, as MT is not perfect, it tends to introduce noise during translation, rendering textual embeddings corrupted and thereby compromising the retrieval performance. To alleviate this, we propose Noise-Robust Fine-tuning (NRF) which tries to extract clean textual information from a possibly noisy target-language input with the guidance of its source-language counterpart. Besides, contrastive learning involving different modalities are performed to strengthen the noise-robustness of our model. Different from traditional cross-modal retrieval methods which only employ image/video-text paired data for fine-tuning, in NRF, selected parallel data plays a key role in improving the noise-filtering ability of our model. Extensive experiments are conducted on three video-text and image-text retrieval benchmarks across different target languages, and the results demonstrate that our method significantly improves the overall performance without using any image/video-text paired data on target languages.

Smart contract vulnerability detection draws extensive attention in recent years due to the substantial losses caused by hacker-attacks. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which is labor-intensive and non-scalable. More importantly, expert-defined rules tend to be error-prone and suffer the inherent risk of being cheated by crafty attackers. Recent researches focus on the symbolic execution and formal analysis of smart contract for vulnerability detection, yet to achieve a precise and scalable solution. Although several methods have been proposed to detect vulnerabilities in smart contracts, there is still a lack of effort that considers combining expert-defined security patterns with deep neural networks. In this paper, we explore using graph neural networks and expert knowledge for smart contract vulnerability detection. Specifically, we cast the rich control- and data- flow semantics of the source code into a contract graph. Then, we propose a novel temporal message propagation network to extract graph feature from the normalized graph, and combine the graph feature with expert patterns to yield a final detection system. Extensive experiments are conducted on all the smart contracts that have source code in two platforms. Empirical results show significant accuracy improvements over state-of-the-art methods.

Community detection is a fundamental problem in graph analysis, while most existing research focuses on unsigned graphs. In many applications, networks involve both positive and negative connections. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs by leveraging the concept of balance theory. We show that the problem of finding the maximum stable k-core is NP-hard. Advanced approaches are proposed to accelerate the processing. Experiments on 6 signed networks are conducted to verify the efficiency and effectiveness of proposed model and techniques.

Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable$k$-core, to measure the stability of a community in signed graphs. The stable$k$-core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable$k$-core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural network models, deep visual features are widely used for video content representation. However, as how two videos are relevant is task-dependent, such off-the-shelf features are not always optimal for all tasks. Moreover, due to varied concerns including copyright, privacy and security, one might have access to only pre-computed video features rather than original videos. We propose in this paper feature re-learning for improving video relevance prediction, with no need of revisiting the original video content. In particular, re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We optimize the re-learning process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, we propose a new data augmentation strategy which works directly on frame-level and video-level features. Extensive experiments in the context of the Hulu Content-based Video Relevance Prediction Challenge 2018 justify the effectiveness of the proposed method and its state-of-the-art performance for content-based video relevance prediction.

The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries.

Rare behaviors indicate important events and situations in maritime surveillance applications. State-of-the-art methods provide many effective solutions to detect anomalous behaviors. Meanwhile, most solutions are parameter-laden and too costly to identify useful rare behaviors with human knowledge in a visual analytics manner. This paper is concerned with a scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification. Although the scheme involves several parameters, we demonstrate that they are able to be tackled in thresholds. As a result, a rare behavior factor is the single parameter that affect the detecting results. The proposed scheme is evaluated via a simulated data set for performance and a real life AIS data for effectiveness. Results show that high accuracy to labelled anomalies and useful rare behaviors can be achieved.

In the field of pattern recognition, data analysis, and machine learning, data points are usually modeled as high-dimensional vectors. Due to the curse-of-dimensionality, it is non-trivial to efficiently process the orginal data directly. Given the unique properties of nonlinear dimensionality reduction techniques, nonlinear learning methods are widely adopted to reduce the dimension of data. However, existing nonlinear learning methods fail in many real applications because of the too-strict requirements (for real data) or the difficulty in parameters tuning. Therefore, in this paper, we investigate the manifold learning methods which belong to the family of nonlinear dimensionality reduction methods. Specifically, we proposed a new manifold learning principle for dimensionality reduction named Curved Cosine Mapping (CCM). Based on the law of cosines in Euclidean space, CCM applies a brand new mapping pattern to manifold learning. In CCM, the nonlinear geometric relationships are obtained by utlizing the law of cosines, and then quantified as the dimensionality-reduced features. Compared with the existing approaches, the model has weaker theoretical assumptions over the input data. Moreover, to further reduce the computation cost, an optimized version of CCM is developed. Finally, we conduct extensive experiments over both artificial and real-world datasets to demonstrate the performance of proposed techniques.