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Claim this profileWorks102 from public data
- Unsupervised Person Re-identification668
A progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains and demonstrates that PUL outputs discriminative features that improve the re-ID accuracy.
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- PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences168
The proposed PST convolution first disentangles space and time in point cloud sequences, then a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal Convolution is used to model the dynamics of the spatial regions along the time dimension.
- Adaptive Exploration for Unsupervised Person Re-identification154
An Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner and integrates a balance strategy into the adaptive selection.
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- Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification91
An end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames by incorporating an adaptive stop network to measure confidence score and generate timely trigger to stop the agent watching videos, which improves efficiency without loss of accuracy.
- Cubic LSTMs for Video Prediction90
Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.
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- Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition76
A novel Prototypical Calibrating Ambiguous Network (PCAN) is proposed to unleash and mitigate the ambiguity of MAR and proposes a prototype-guided rectification to rectify prediction by incorporating the representability of prototypes.
- VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing70
VideoGrain is presented, a zero-shot approach that modulates space-time (cross- and self-) attention mechanisms to achieve fine-grained control over video content and improves feature separation by increasing intra-region awareness and reducing inter-region interference in self-attention.
- Uncovering what, why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly70
MMEval is introduced, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anoma-lies.
- Attract or Distract: Exploit the Margin of Open Set67
This paper exploits the semantic structure of open set data from two aspects: 1)Semantic Categorical Alignment, which aims to achieve good separability of target known classes by categorically aligning the centroid of target with the source, and 2) Semantic Contrastive Mapping,Which aims to push the unknown class away from the decision boundary.
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- STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition53
A novel framework STPrivacy is proposed to perform video-level PPAR and introduces vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i.e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
- Recurrent Attention Network with Reinforced Generator for Visual Dialog51
A dialog network is used to memorize the temporal context and an attention processor to parse the spatial context in Visual Dialog to reinforce G at the sentence level using the discriminative model (D), which aims to select the right answer from a few candidates to ameliorate the problem.
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- Cascaded Revision Network for Novel Object Captioning42
This paper proposes a novel network structure, Cascaded Revision Network, which aims at relieving the problem of inaccurate captioning by equipping the model with out-of-domain knowledge and can accurately describe images with unseen objects.
- Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition37
A novel Motion-guided Modulation Network (MMN) is presented that implicitly captures and modulates subtle motion cues to enhance spatial-temporal representation learning and achieves state-of-the-art performance in skeleton-based micro-action recognition.
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- EnergyMogen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space36
This work proposes EnergyMoGen, which includes two spectrums of Energy-Based Models, and introduces Synergistic Energy Fusion, a semantic-aware energy model based on cross-attention, which enables semantic composition and adaptive gradient descent for text embeddings.
- Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction35
This paper proposes a Masked Local Structure Prediction method to encode target data and enables models to embed source and target data in a shared feature space through estimating point cardinality, position and normal.
- DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization33
This paper proposes DreamDPO, an optimization-based framework that integrates human preferences into the 3D generation process, through direct preference optimization, and achieves competitive results, and provides higher-quality and more controllable 3D content compared to existing methods.
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 2026-10-12. 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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