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- Zhengyou ZhangSuggested from co-authorship
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Works240 from public data
- ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression1,895
ThiNet is proposed, an efficient and unified framework to simultaneously accelerate and compress CNN models in both training and inference stages, and it is revealed that it needs to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
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- PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments314
A novel loss, Pixels-IoU (PIoU) Loss, is formulated to exploit both the angle and IoU for accurate OBB regression and can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
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- Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition230
An Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro- expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vectorthrough a Long Short-term Memory (LSTM) module.
- A Short Survey of Recent Advances in Graph Matching223
The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.
- Multiple Sound Sources Localization from Coarse to Fine192
A two-stage audiovisual learning framework that disentangles audio and visual representations of different categories from complex scenes, then performs cross-modal feature alignment in a coarse-to-fine manner achieves state-of-the-art results on public dataset of localization, as well as considerable performance on multi-source sound localization in complex scenes.
- Person Re-Identification with Correspondence Structure Learning175
A boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair is introduced, which integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images.
- ATRW165
A novel method for tiger re-identification is proposed, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods.
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- Towards Accurate One-Stage Object Detection With AP-Loss134
A novel optimization algorithm is developed, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks, and good convergence property of the proposed algorithm is verified theoretically and empirically.
- Survey on blind image forgery detection133
This survey attempts to cover the blind techniques that have been proposed for exposing forgeries, focusing on the detection techniques for three of the most common forgery types, namely copy/move, splicing and retouching.
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- Deep Neural Network Compression With Single and Multiple Level Quantization125
This paper is the first to consider the network quantization from both width and depth level, and introduces incremental layer compensation to quantize layers iteratively which decreases the quantization loss in each iteration.
- Learning vision-based agile flight via differentiable physics123
An end-to-end approach that combines deep learning with first-principles physics through differentiable simulation to enable autonomous navigation by several aerial robots through complex environments at high speed is introduced.
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- TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition106
A Two-stage Action Alignment Network (TA2N) is addressed sequentially through a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature.
- HiEve: A Large-Scale Benchmark for Human-Centric Video Analysis in Complex Events103
A new large-scale dataset with comprehensive annotations, named HiEve, for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd and complex events, and it contains a record number of poses, the largest number of action instances, and one of the largest numbers of trajectories lasting for longer time.
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- AP-Loss for Accurate One-Stage Object Detection85
A novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem is proposed, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
- TRP: Trained Rank Pruning for Efficient Deep Neural Networks80
The proposed Trained Rank Pruning (TRP), which alternates between low rank approximation and training, is proposed, which maintains the capacity of the original network while imposing low-rank constraints during training.
- HAC++: Towards 100X Compression of 3D Gaussian Splatting75
This paper proposes HAC++, which explicitly minimizes the 3D Gaussian Splatting representation’s entropy during optimization, enabling efficient arithmetic coding after training for compressed storage and incorporating an adaptive masking strategy to eliminate non-effective Gaussians and anchors.
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- Activity Recognition Using a Combination of Category Components and Local Models for Video Surveillance72
This paper proposes to represent an activity by a combination of category components and demonstrates that this approach offers flexibility to add new activities to the system and an ability to deal with the problem of building models for activities lacking training data.
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- Fast Feedforward 3D Gaussian Splatting Compression67
This work introduces Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds.
- Group Event Detection With a Varying Number of Group Members for Video Surveillance67
A group representative is proposed to use to handle the recognition with a varying number of group members, and an asynchronous hidden Markov model (AHMM) is used to model the relationship between people.
- A Diffusion and Clustering-Based Approach for Finding Coherent Motions and Understanding Crowd Scenes63
This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining and introduces a cluster-and-merge process.
- Learning Correspondence Structures for Person Re-Identification62
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification by introducing a boosting-based approach to learn a correspondence structure, which indicates the patchwise matching probabilities between images from a target camera pair and introduces a global constraint-based matching process.
- A Heat-Map-Based Algorithm for Recognizing Group Activities in Videos62
A new heat-map-based algorithm that first models human trajectories as series of heat sources and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities for activity recognition is proposed.
- Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis60
An abnormality-type-based method which creates short-period summary videos from long-period input surveillance videos by properly arranging abnormal blob sequences according to their activity types is proposed.
- A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories59
This paper addresses the problem of representing motion trajectories in a highly informative way, and utilizes the droplet-based process for analyzing trajectories and applies it to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition.
- Class-Aware Sounding Objects Localization via Audiovisual Correspondence53
A two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision, which is superior in localizing and recognizing objects as well as filtering out silent ones.
- Group Re-Identification53
Multi-grain representations are computed to characterize the appearance and spatial features of multi-grain objects and evaluate the importance weight of each object for group Re-ID, so as to handle the interferences from group dynamics.
- Group Reidentification with Multigrained Matching and Integration50
This paper introduces multigrained representations which can be extracted via the development of two separate schemes, that is, one with handcrafted descriptors and another with deep neural networks, and is further equipped with importance weights which capture variations in intragroup dynamics.
- Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach48
A coarse-to-fine thermal-diffusion-based approach to detect coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition, and introduces a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions.
- Spatio-Temporal Point Process for Multiple Object Tracking47
This work proposes a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories for multiple object tracking and adopts the spatio-temporal point process model to model such events.
- Region-Based Rate Control for H.264/AVC for Low Bit-Rate Applications47
Experimental results demonstrate that the proposed region-based rate control approach can achieve both better subjective and objective quality by performing the rate control adaptively with the content, compared to the conventional rate control approaches.
- Variational Pedestrian Detection44
A novel and efficient algorithm for pedestrian detection is formulated by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm, which can be fashioned into a variational pedestrian detector.
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- Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization43
A multi-level feature optimization framework to improve the generalization and temporal modeling ability of learned video representations and a simple temporal modeling module from multi- level features to enhance motion pattern learning is proposed.
- A Fast Sub-Pixel Motion Estimation Algorithm for H.264/AVC Video Coding43
A novel fast sub-pixel ME algorithm is proposed which performs a “rough” sub- pixel search before the partition selection, and performs a "precise" sub-Pixel search for the best partition.
- SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking42
A novel siamese tracking algorithm called SiamRCR is proposed, addressing this problem with a simple, light and effective solution, and builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample.
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- Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion38
This paper introduces a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions, and introduces an asynchronous fusion network which fuses information from different streams by asynchronously integrating stream-wise features in different time points.
- Macroblock Classification Method for Video Applications Involving Motions37
Experimental results demonstrate that the methods based on the proposed approach can work effectively on these applications, including shot change detection, motion discontinuity detection, and outlier rejection for global motion estimation.
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- Towards Reversal-Invariant Image Representation35
A reversal-invariant version of SIFT descriptor named Max-SIFT, a generalized RIDE algorithm which can be applied to a large family of local descriptors which reveals consistent accuracy gain on various image classification tasks, including scene understanding, fine-grained object recognition, and large-scale visual recognition.
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- PCGS: Progressive Compression of 3D Gaussian Splatting26
This work proposes PCGS (Progressive Compression of 3D Gaussian Splatting), which adaptively controls both the quantity and quality of Gaussians (or anchors) to enable effective progressivity for on-demand applications.
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- LSTC: Boosting Atomic Action Detection with Long-Short-Term Context7
This paper place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporalreliance among video signals affect the action detection results, and demonstrates that both temporal grains are beneficial to atomic action recognition.
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- Pedestrian Detection with a Directly-Cascaded Deconvolution-Convolution Structure–
A novel neural network structure which can be flexibly combined with powerful object detection systems for boosting pedestrian detection and incorporates the features learned in different layers in a novel and unified fashion is proposed.
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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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