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Claim this profileWorks336 from public data
- Heterogeneous Graph Attention Network3,463
Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of the proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
- The rise and potential of large language model based agents: a survey906
A comprehensive survey on LLM-based agents is conducted, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based agents.
- Uncovering social network sybils in the wild460
It is found that contrary to prior conjecture, Sybil accounts in OSNs do not form tight-knit communities, Instead, they integrate into the social graph just like normal users.
- Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey336
This work introduces the background of multi-modal pre-training by reviewing the conventional deep learning, pre- training works in natural language process, computer vision, and speech, and discusses the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre- Training.
- Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark324
A new benchmark specifically dedicated to the tracking-by-language, including a large scale dataset, strong and diverse baseline methods, and a strong baseline method based on an adaptive local-global-search scheme is proposed for future works to compare.
- Uncovering social network Sybils in the wild290
These efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN) are described and it is shown that Sybils can effectively avoid existing community-based Sybil detectors.
- A pyroptosis nanotuner for cancer therapy287
An acid-activatable nanophotosensitizer library is reported that can be used to spatiotemporally target distinct stages of endosomal maturation, enabling tunable cellular pyroptosis.
- VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows271
This work proposes a large-scale Visible-Event benchmark (termed VisEvent) and builds a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data.
- Learn to Match: Automatic Matching Network Design for Visual Tracking256
This work introduces six novel matching operators from the perspective of feature fusion instead of explicit similarity learning, namely Concatenation, Pointwise-Addition, Pairwise-Relation, FiLM, Simple-Transformer and Transductive-Guidance, to explore more feasibility on matching operator selection.
- Social Turing Tests: Crowdsourcing Sybil Detection225
A large user study on the ability of humans to detect today's Sybil accounts is conducted, using a large corpus of ground-truth Sybils from the Facebook and Renren networks and finds that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results.
- Arbitrary-Order Proximity Preserved Network Embedding210
The eigen-decomposition reweighting theorem is theoretically proved, revealing the intrinsic relationship between proximities of different orders and proposed AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework.
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- AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries175
The proposed Adversarial Contrastive (AdCo) model not only achieves superior performances, but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs.
- Pedestrian attribute recognition: A survey163
The background of pedestrian attribute recognition (PAR), including the fundamental concepts of pedestrian attributes and corresponding challenges are introduced, and existing benchmarks, including popular datasets and evaluation criterion are introduced.
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- Drug repurposing screening and mechanism analysis based on human colorectal cancer organoids146
Human CRC tumor-derived organoids are established that well represent both morphological and molecular heterogeneities of original tumors and anticancer activities of drug candidates were further validated in the established patient-derived organoids-based xenograft (PDOX) system in vivo.
- Event-based Video Reconstruction via Potential-assisted Spiking Neural Network140
This paper proposes a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron, and finds that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks.
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- VcT: Visual Change Transformer for Remote Sensing Image Change Detection115
It is demonstrated that carefully mining the common background information provides an important cue to learn the consistent representations for the two images which thus obviously facilitates the visual change detection problem.
- Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification108
A novel Cross-scale Interactive Feature Extractor (CIFE) is introduced to extract and fuse different scale CNN features as an effective backbone module for the proposed few-shot learning method.
- Large-Scale Spatio-Temporal Person Re-Identification: Algorithms and Benchmark108
This work contributes a novel Large-scale Spatio-Temporal (LaST) person re- ID dataset, including 10,862 identities with more than 228k images, and proposes an easy-to-implement baseline that works well in such challenging re-ID settings.
- Semantic-Guided Pixel Sampling for Cloth-Changing Person Re-Identification102
A semantic-guided pixel sampling approach for the cloth-changing person re-ID task that outperforms previous methods with a large margin and adopts a loss function to constrain the learned features to keep consistent before and after changes.
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- A color image encryption and hiding algorithm based on hyperchaotic system and discrete cosine transform101
Simulation analysis and security evaluation demonstrate that the proposed color image encryption and hiding algorithm has better encryption performance and higher security than several advanced image encryption algorithms.
- cmSalGAN: RGB-D Salient Object Detection With Cross-View Generative Adversarial Networks95
cmSalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem.
- MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking88
A new dynamic modality-aware filter generation module (named MFGNet) is proposed to boost the message communication between visible and thermal data by adaptively adjusting the convolutional kernels for various input images in practical tracking.
- Learning Spatial-Frequency Transformer for Visual Object Tracking86
A unified Spatial-Frequency Transformer that models the Gaussian spatial Prior and High-frequency emphasis Attention (GPHA) simultaneously is proposed simultaneously and is integrated into the Siamese tracking framework and proposed a novel tracking algorithm termed SFTransT.
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- Compact Graph Structure Learning via Mutual Information Compression75
This paper theoretically prove that if the authors optimize basic views and final view based on mutual information, and keep their performance on labels simultaneously, the final view will be a minimal sufficient structure, named CoGSL.
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- Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift68
A novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset and apply the loss correction method to improve the quality of pseudo labels.
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- Prompt-Based Learning for Unpaired Image Captioning56
This research attempts to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task, and introduces a semantic prompt and aggregated with visual features for more accurate caption prediction under the adversarial learning framework.
- Dynamic Attention Guided Multi-Trajectory Analysis for Single Object Tracking56
This paper proposes to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy that contains multiple target templates, each of which provides its own attention for locating the target in the new frame.
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- Triple-image bit-level encryption algorithm based on double cross 2D hyperchaotic map53
Simulation results and security analysis indicate that the proposed novel triple-image bit-level encryption algorithm can encrypt three images simultaneously and has better security than several state-of-the-art encryption algorithms.
- Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses48
This paper proposes hierarchical random switching (HRS), which protects neural networks through a novel randomization scheme, and proposes Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense.
- Tiny Object Tracking: A Large-Scale Dataset and a Baseline46
A novel multilevel knowledge distillation network (MKDNet) is proposed, which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination, and localization abilities in tracking tiny objects.
- Tracking by Joint Local and Global Search: A Target-Aware Attention-Based Approach46
A novel and general target-aware attention mechanism (termed TANet) is proposed and integrated with a tracking-by-detection framework to conduct joint local and global search for robust tracking and resorts to adversarial training for better attention prediction.
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- Deep learning-based classification and anomaly detection of side-channel signals42
This paper investigates the application of recent deep learning techniques to side-channel analysis in both classification of machine state and anomaly detection and shows the potential of using thesedeep learning techniques in side- channel analysis and cyber-attack detection.
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- Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization36
Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization AMVNMF is proposed, which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced.
- An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos34
A stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods and a graphical user interface (GUI) was designed so that researchers could use this automatic analysis module smoothly.
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- When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark32
This paper builds a novel pedestrian detection dataset from the nighttime surveillance aspect, and compares the performances of state-of-the-art pedestrian detectors and reveals that the methods cannot solve all the challenging problems of NightSurveillance.
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- Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search28
A novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking, which is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm.
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- Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition14
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- Entropy-based active sparse subspace clustering10
A novel extension for SSC with active learning framework is proposed, in which the most informative pairwise constraints are selected to guide the SSC for accurate clustering results.
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- A heuristically accelerated reinforcement learning method for maintenance policy of an assembly line7
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- MFDA-YOLO: A multiscale feature fusion and dynamic alignment network for UAV small objects detection2
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- WHU-NERCMS at TRECVID2014: Instance Search Task.2
The work at the automatic instance search task of TRECVID 2014 is divided into two parts: first part is object retrieval based on BOW, and secondly, several optimization strategies are adopted to improve the initial results.
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- Design and application of blockchain power data transaction information protection based on paillier–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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