619 citations · 6.4k across the 178 of their papers we have counts for
7 papers · 1 filter
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
Yupeng Zhou, Daquan Zhou, Zuo-Liang Zhu +3
Recent advancements in diffusion models have showcased their impressive capacity to generate visually striking images. Nevertheless, ensuring a close match between the generated im…
MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation
Hanshu Yan, Jun Hao Liew, Long Mai +2
This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the v…
DOAD: Decoupled One Stage Action Detection Network
Shuning Chang, Pichao Wang, Fan Wang +2
Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stag…
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
Yabo Zhang, Zihao Wang, Jun Hao Liew +4
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised me…
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Kunyang Han, Yong Liu, Jun Hao Liew +8
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred…
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
Ruyang Liu, Jingjia Huang, Ge Li +3
Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention f…
Temporal Perceiving Video-Language Pre-training
Fan Ma, Xiaojie Jin, Heng Wang +4
Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve g…