Autonomous exploration in unknown environments is a crucial challenge for various applications of unmanned aerial vehicles (UAVs). However, in large-scale scenarios, existing methods suffer from inefficient environmental information acquisition, computationally expensive exploration planning, and inconsistent motion. In this work, we present a novel method for rapid UAV autonomous exploration in large-scale environments. We develop a surface frontier guided viewpoints generation strategy that supports efficient coverage of scenario. Besides, we introduce an incremental viewpoint clustering method to approximate distant viewpoints using fewer anchor points, decreasing the computational costs of exploration tour planning. Building upon this, we propose a history-informed tour planning method that incorporates information from previous tour into the optimization process, maintaining motion consistency. Extensive simulation experiments validate that our method outperforms existing state-of-the-art methods in terms of exploration time, travel distance, and run time. Various real-world experiments are conducted to indicate the practicality of our approach. The source code will be released to benefit the community1.

Autonomous exploration is essential for the effective deployment of quadrotors in various applications. However, existing approaches face significant challenges in large-scale environments, particularly in balancing global coverage efficiency and computational overhead. These limitations often result in poor adaptability to environmental changes and redundant revisits to previously explored areas, reducing overall exploration efficiency. To address these issues, we propose FASTEX, a fast UAV exploration framework designed for large-scale environments, using dynamically expanding grids and coverage paths to improve exploration efficiency. To support efficient exploration planning in large-scale scenarios, we introduce an efficient environment preprocessing method, including a dynamic grid expansion mechanism and a sparse roadmap. Furthermore, we present a hierarchical exploration planning framework that integrates an incremental global planner with a local planner, ensuring high coverage and computational efficiency. Extensive simulation tests demonstrate the superior performance and robustness of the proposed method compared to the state-of-the-art methods. In addition, we conduct various real-world experiments to validate the feasibility of our autonomous exploration system.

Bilinear pooling has achieved state-of-the-art performance on fusing features in various machine learning tasks, owning to its ability to capture complex associations between features. Despite the success, bilinear pooling suffers from redundancy and burstiness issues, mainly due to the rank-one property of the resulting representation. In this paper, we prove that bilinear pooling is indeed a similarity-based coding-pooling formulation. This establishment then enables us to devise a new feature fusion algorithm, the factorized bilinear coding (FBC) method, to overcome the drawbacks of the bilinear pooling. We show that FBC can generate compact and discriminative representations with substantially fewer parameters. Experiments on two challenging tasks, namely image classification and visual question answering, demonstrate that our method surpasses the bilinear pooling technique by a large margin.

Exploiting relationships between objects for image and video captioning has received increasing attention. Most existing methods depend heavily on pre-trained detectors of objects and their relationships, and thus may not work well when facing detection challenges such as heavy occlusion, tiny-size objects, and long-tail classes. In this paper, we propose a joint commonsense and relation reasoning method that exploits prior knowledge for image and video captioning without relying on any detectors. The prior knowledge provides semantic correlations and constraints between objects, serving as guidance to build semantic graphs that summarize object relationships, some of which cannot be directly perceived from images or videos. Particularly, our method is implemented by an iterative learning algorithm that alternates between 1) commonsense reasoning for embedding visual regions into the semantic space to build a semantic graph and 2) relation reasoning for encoding semantic graphs to generate sentences. Experiments on several benchmark datasets validate the effectiveness of our prior knowledge-based approach.

Most existing Visual Question Answering (VQA) models overly rely on language priors between questions and answers. In this paper, we present a novel method of language attention-based VQA that learns decomposed linguistic representations of questions and utilizes the representations to infer answers for overcoming language priors. We introduce a modular language attention mechanism to parse a question into three phrase representations: type representation, object representation, and concept representation. We use the type representation to identify the question type and the possible answer set (yes/no or specific concepts such as colors or numbers), and the object representation to focus on the relevant region of an image. The concept representation is verified with the attended region to infer the final answer. The proposed method decouples the language-based concept discovery and vision-based concept verification in the process of answer inference to prevent language priors from dominating the answering process. Experiments on the VQA-CP dataset demonstrate the effectiveness of our method.

Generating stylized captions for images is a challenging task since it requires not only describing the content of the image accurately but also expressing the desired linguistic style appropriately. In this paper, we propose MemCap, a novel stylized image captioning method that explicitly encodes the knowledge about linguistic styles with memory mechanism. Rather than relying heavily on a language model to capture style factors in existing methods, our method resorts to memorizing stylized elements learned from training corpus. Particularly, we design a memory module that comprises a set of embedding vectors for encoding style-related phrases in training corpus. To acquire the style-related phrases, we develop a sentence decomposing algorithm that splits a stylized sentence into a style-related part that reflects the linguistic style and a content-related part that contains the visual content. When generating captions, our MemCap first extracts content-relevant style knowledge from the memory module via an attention mechanism and then incorporates the extracted knowledge into a language model. Extensive experiments on two stylized image captioning datasets (SentiCap and FlickrStyle10K) demonstrate the effectiveness of our method.

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.

Opinion mining focuses on extracting customers' opinions from the reviews and predicting their sentiment orientation. Reviewers usually praise a product in some aspects and bemoan it in other aspects. With the business globalization, it is very important for enterprises to extract the opinions toward different aspects and find out cross-lingual/cross-culture difference in opinions. Cross-lingual opinion mining is a very challenging task as amounts of opinions are written in different languages, and not well structured. Since people usually use different words to describe the same aspect in the reviews, product-feature (PF) categorization becomes very critical in cross-lingual opinion mining. Manual cross-lingual PF categorization is time consuming, and practically infeasible for the massive amount of data written in different languages. In order to effectively find out cross-lingual difference in opinions, we present an aspect-oriented opinion mining method with Cross-lingual Latent Semantic Association (CLaSA). We first construct CLaSA model to learn the cross-lingual latent semantic association among all the PFs from multi-dimension semantic clues in the review corpus. Then we employ CLaSA model to categorize all the multilingual PFs into semantic aspects, and summarize cross-lingual difference in opinions towards different aspects. Experimental results show that our method achieves better performance compared with the existing approaches. With CLaSA model, our text mining system OpinionIt can effectively discover cross-lingual difference in opinions.

We are concerned with the problem of similarity joins of text data, where the task is to find all pairs of documents above an expected similarity. Such a problem often serves as an indispensable step in many web applications. A crucial issue is to preclude unnecessary candidate pairs as many as possible ahead of expensive similarity evaluation. In this paper, we initiate an idea of adopting a cascade structure in text joins for a large speedup, where a latter stage can exclude a considerable number of invalid pairs survived in former stages. The proposed algorithm is shortly referred to as CasJoin. We further adopt a prefix filter to build the stage of CasJoin by introducing a novel vision to the dynamic generation of document vector. Specifically, a vector is partitioned into a chain of multiple prefixes that are appended one by one for cascade joining. We evaluate our CasJoin on a typical web corpus, ODP. Experiments indicate that, comparing to the state-of-the-art prefix algorithms, CasJoin can achieve a drastic reduction of candidates by as much as 98.15% and a dramatic speedup of joining by up to 13.34x.

In recent years, the number of freely available online reviews is increasing at a high speed. Aspect-based opinion mining technique has been employed to find out reviewers' opinions toward different product aspects. Such finer-grained opinion mining is valuable for the potential customers to make their purchase decisions. Product-feature extraction and categorization is very important for better mining aspect-oriented opinions. Since people usually use different words to describe the same aspect in the reviews, product-feature extraction and categorization becomes more challenging. Manually product-feature extraction and categorization is tedious and time consuming, and practically infeasible for the massive amount of products. In this paper, we propose an unsupervised product-feature categorization method with multilevel latent semantic association. After extracting product-features from the semi-structured reviews, we construct the first latent semantic association (LaSA) model to group words into a set of concepts according to their virtual context documents. It generates the latent semantic structure for each product-feature. The second LaSA model is constructed to categorize the product-features according to their latent semantic structures and context snippets in the reviews. Experimental results demonstrate that our method achieves better performance compared with the existing approaches. Moreover, the proposed method is language- and domain-independent.

Web document could be seen to be composed of textual content as well as social metadata of various forms (e.g., anchor text, search query and social annotation), both of which are valuable to indicate the semantic content of the document. However, due to the free nature of the web, the two streams of web data suffer from the serious problems of noise and sparseness, which have actually become the major challenges to the success of many web mining applications. Previous work has shown that it could enhance the content of web document by integrating anchor text and search query. In this paper, we study the problem of exploring emergent social annotation for document enhancement and propose a novel reinforcement framework to generate "social representation" of document. Distinguishing from prior work, textual content and social annotation are enhanced simultaneously in our framework, which is achieved by exploiting a kind of mutual reinforcement relationship behind them. Two convergent models, social content model and social annotation model, are symmetrically derived from the framework to represent enhanced textual content and enhanced social annotation respectively. The enhanced document is referred to as Social Document or sDoc in that it could embed complementary viewpoints from many web authors and many web visitors. In this sense, the document semantics is enhanced exactly by exploring social wisdom. We build the framework on a large Del.icio.us data and evaluate it through three typical web mining applications: annotation, classification and retrieval. Experimental results demonstrate that social representation of web document could boost the performance of these applications significantly.

Address standardization is a very challenging task in data cleansing. To provide better customer relationship management and business intelligence for customer-oriented cooperates, millions of free-text addresses need to be converted to a standard format for data integration, de-duplication and householding. Existing commercial tools usually employ lots of hand-craft, domain-specific rules and reference data dictionary of cities, states etc. These rules work better for the region they are designed. However, rule-based methods usually require more human efforts to rewrite these rules for each new domain since address data are very irregular and varied with countries and regions. Supervised learning methods usually are more adaptable than rule-based approaches. However, supervised methods need large-scale labeled training data. It is a labor-intensive and time-consuming task to build a large-scale annotated corpus for each target domain. For minimizing human efforts and the size of labeled training data set, we present a free-text address standardization method with latent semantic association (LaSA). LaSA model is constructed to capture latent semantic association among words from the unlabeled corpus. The original term space of the target domain is projected to a concept space using LaSA model at first, then the address standardization model is active learned from LaSA features and informative samples. The proposed method effectively captures the data distribution of the domain. Experimental results on large-scale English and Chinese corpus show that the proposed method significantly enhances the performance of standardization with less efforts and training data.

The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features as opposed to the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without the explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.

This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applications usually need to provide online service for user and deal with large volume of data, classification speed emerges as an important issue to be addressed. In face detection, Asymmetric Cascade is used to speed up imbalanced classification by building a cascade structure of simple classifiers, but it often causes a loss of classification accuracy due to the iterative feature addition in its learning procedure. In this paper, we adopt the idea of cascade classifier in imbalanced web mining for fast classification and propose a novel asymmetric cascade learning method called FloatCascade to improve the accuracy. To the end, FloatCascade selects fewer yet more effective features at each stage of the cascade classifier. In addition, a decision-tree scheme is adopted to enhance feature diversity and discrimination capability for FloatCascade learning. We evaluate FloatCascade through two typical IC applications in web mining: web page categorization and citation matching. Experimental results demonstrate the effectiveness and efficiency of FloatCascade comparing to the state-of-the-art IC methods like Asymmetric Cascade, Asymmetric AdaBoost and Weighted SVM.

In this paper, we propose a novel local steerable phase (LSP) feature extracted from the face image using steerable filter for face representation and recognition. Steerable filter is a kind of oriented filters. It is rotated very efficiently by taking a suitable linear combination of basis filters and allows adaptive control over phase as well as orientation. Phase information provided by steerable filter is locally stable with respect to scale, noise and brightness changes. Furthermore, steerable filter is implemented within a Gaussian pyramid to make use of discriminative power in the scale-space of face images. Each face is represented as multiple "steerablefaces" of different scales and orientations. With simple down-sampling, all the steerablefaces are concatenated to an augmented feature vector for evaluating similarity between face images. A nearest-neighbor classifier based on local weighted phase-correlation is used for final decision rule. Experimental results on FERET and XM2VTS databases demonstrate the performance of the proposed method.

In this paper, facial expression is coded by steerable filters which are rotated very efficiently by taking a suitable linear combination of basis filters. Local features extracted by steerable filters are locally stable with respect to scale, noise, and brightness changes, and distinctive enough to capture subtle facial expression cues. Further more, steerable filters are implemented within a Gaussian pyramid to exploit discriminative power in scale-space. Responses of the filters are concatenated to an augmented feature vector to evaluate the similarity between different facial expression images with the nearest-neighbor rule for final decisions. In comparison with Gabor filters, steerable filters save much computational cost and obtain comparable recognition performance with fewer features. Experiments on the JAFFE database demonstrate the effectiveness of steerable filters for coding facial expression.