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Works89 from public data
- 398
- Person Re-identification via Recurrent Feature Aggregation253
This work shows that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation.
- Learning Context Graph for Person Search209
This work proposes a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene, and builds a graph learning framework to effectively employ context pairs to update target similarity.
- Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification162
This work proposes a novel graph-based framework, namely Multi-Granular Hypergraph (MGH), to pursue better representational capabilities by modeling spatiotemporal dependencies in terms of multiple granularities to enhance the overall video representation.
- Video Summarization via Semantic Attended Networks154
A semantic attended video summarization network (SASUM) which consists of a frame selector and video descriptor to select an appropriate number of video shots by minimizing the distance between the generated description sentence of the summarized video and the human annotated text of the original video.
- Anchor-Free Person Search132
This work presents the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task, and proposes an aligned feature aggregation module to generate more discriminative and robust feature embeddings by following a "re-id first" principle.
- Inter-X: Towards Versatile Human-Human Interaction Analysis126
This work proposes Inter-X, a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns, together with detailed hand gestures, and proposes a unified bench-mark composed of 4 categories of downstream tasks from both the perceptual and generative directions.
- ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation110
A GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions, and introduces a new synthetic dataset of complex multi-person combat behaviors.
- Skeleton-Aided Articulated Motion Generation96
This work makes the first attempt to generate articulated human motion sequence from a single image by utilizing paired inputs including human skeleton information as motion embedding and a single human image as appearance reference to generate novel motion frames based on the conditional GAN infrastructure.
- Learning Multi-Attention Context Graph for Group-Based Re-Identification71
A novel unified framework based on graph neural networks is proposed to simultaneously address the above two group-based re-id tasks, i.e., group re-ID and group-aware person re-ids, and a context graph with group members as its nodes to exploit dependencies among different people is constructed.
- ReGenNet: Towards Human Action-Reaction Synthesis67
Quantitative and qualitative results show that the proposed diffusion-based generative model with a Trans-former decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner, where the future states of actors are unavailable to reactors.
- EAN: Event Adaptive Network for Enhanced Action Recognition64
A unified action recognition framework to investigate the dynamic nature of video content by introducing the following designs, called the proposed framework as Event Adaptive Network because both key designs are adaptive to the input video content.
- A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding50
By introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of the approach is further enhanced, and a low-bitrate video understanding benchmark is built with three downstream tasks on eight datasets, demonstrating the notable superiority of the approach.
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- Exploring Visual Context for Weakly Supervised Person Search45
This paper inventively considers weakly supervised person search with only bounding box annotations, and discovers three levels of context clues in unconstrained natural images that promote local and global discriminative capabilities and enhances clustering accuracy.
- DialogueNeRF: towards realistic avatar face-to-face conversation video generation43
A novel unified framework based on the neural radiance field (NeRF) is proposed to address the challenges of conversation generation and demonstrates that the avatars in the resulting videos are able to carry on a realistic conversation, and maintain individual styles.
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- MMHead: Towards Fine-grained Multi-modal 3D Facial Animation34
A simple but efficient VQ-VAE-based method named MM2Face is proposed to unify the multi-modal information and generate diverse and plausible 3D facial motions, which achieves competitive results on both benchmarks.
- GANHead: Towards Generative Animatable Neural Head Avatars32
GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations, achieves superior performance on head avatar generation and raw scan fitting.
- Efficient Person Search: An Anchor-Free Approach32
This work presents an anchor-free approach to efficiently tackling person search, and names its framework as Feature-Aligned Person Search Network (AlignPS), which achieves state-of-the-art or competitive performance on two challenging person search benchmarks, but can be also extended to other challenging searching tasks such as animal and object search.
- 28
- Multi-level attention model for person re-identification28
A feature attention block is proposed for person re-identification that can be seamlessly integrated into existing CNN structures, and is trained only with identity loss.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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