219 citations · 509 across the 40 of their papers we have counts for
11 papers · 1 filter
Few-shot Action Recognition with Captioning Foundation Models
Xiang Wang, Shiwei Zhang, Hangjie Yuan +4
Transferring vision-language knowledge from pretrained multimodal foundation models to various downstream tasks is a promising direction. However, most current few-shot action reco…
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
Hangjie Yuan, Shiwei Zhang, Xiang Wang +7
Relational Language-Image Pre-training (RLIP) aims to align vision representations with relational texts, thereby advancing the capability of relational reasoning in computer visio…
ModelScope Text-to-Video Technical Report
Jiuniu Wang, Hangjie Yuan, Dayou Chen +3
This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-t…
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
Huaxin Zhang, Xiang Wang, Xiaohao Xu +3
Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the networ…
Adaptive Semantic Consistency for Cross-domain Few-shot Classification
Hengchu Lu, Yuanjie Shao, Xiang Wang +1
Cross-domain few-shot classification (CD-FSC) aims to identify novel target classes with a few samples, assuming that there exists a domain shift between source and target domains.…
VideoComposer: Compositional Video Synthesis with Motion Controllability
Xiang Wang, Hangjie Yuan, Shiwei Zhang +6
The pursuit of controllability as a higher standard of visual content creation has yielded remarkable progress in customizable image synthesis. However, achieving controllable vide…
BotSCL: Heterophily-aware Social Bot Detection with Supervised Contrastive Learning
Qi Wu, Yingguang Yang, Buyun He +3
Detecting ever-evolving social bots has become increasingly challenging. Advanced bots tend to interact more with humans as a camouflage to evade detection. While graph-based detec…
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +4
Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they gener…
Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition
Jun Cen, Shiwei Zhang, Xiang Wang +4
Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertai…
CLIP-guided Prototype Modulating for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Jun Cen +4
Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explore…
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +4
Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match qu…