11 citations · 21 across the 7 of their papers we have counts for
10 papers
FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions
Peisen Zhao, Xiaopeng Zhang, Mingxing Xu +10
While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. Conventional CLIP-based encode…
GranViT: A Fine-Grained Vision Model With Autoregressive Perception For MLLMs
Guanghao Zheng, Bowen Shi, Mingxing Xu +8
Vision encoders are indispensable for allowing impressive performance of Multi-modal Large Language Models (MLLMs) in vision language tasks such as visual question answering and re…
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Bowen Shi, Peisen Zhao, Zichen Wang +8
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual…
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Shuangrui Ding, Peisen Zhao, Xiaopeng Zhang +3
Transformers have become the primary backbone of the computer vision community due to their impressive performance. However, the unfriendly computation cost impedes their potential…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses
Chen Ju, Peisen Zhao, Ya Zhang +2
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the…