16 citations · 31 across the 9 of their papers we have counts for
9 papers
GenRec: Unifying Video Generation and Recognition with Diffusion Models
Zejia Weng, Xitong Yang, Zhen Xing +2
Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether suc…
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
Zuxuan Wu, Zejia Weng, Wujian Peng +4
Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-…
Towards Scalable Neural Representation for Diverse Videos
Bo He, Xitong Yang, Hanyu Wang +6
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e.g., NeRV, E-NeRV). W…
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Zejia Weng, Xitong Yang, Ang Li +2
Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive zero-shot learning abilities for image understanding, yet limited effort has been made to investigate CLIP…
Vision Transformers Are Good Mask Auto-Labelers
Shiyi Lan, Xitong Yang, Zhiding Yu +3
We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images…
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Bo He, Xitong Yang, Le Kang +3
Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the bou…
Efficient Video Transformers with Spatial-Temporal Token Selection
Junke Wang, Xitong Yang, Hengduo Li +3
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost. In this paper, we present STTS, a toke…
Semi-Supervised Vision Transformers
Zejia Weng, Xitong Yang, Ang Li +2
We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised l…
GTA: Global Temporal Attention for Video Action Understanding
Bo He, Xitong Yang, Zuxuan Wu +3
Self-attention learns pairwise interactions to model long-range dependencies, yielding great improvements for video action recognition. In this paper, we seek a deeper understandin…