7 citations · 24 across the 11 of their papers we have counts for
11 papers
Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation
Peng Jin, Hao Li, Zesen Cheng +6
Text-to-motion generation requires not only grounding local actions in language but also seamlessly blending these individual actions to synthesize diverse and realistic global mot…
GraCo: Granularity-Controllable Interactive Segmentation
Yian Zhao, Kehan Li, Zesen Cheng +6
Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and…
FreestyleRet: Retrieving Images from Style-Diversified Queries
Hao Li, Curise Jia, Peng Jin +5
Image Retrieval aims to retrieve corresponding images based on a given query. In application scenarios, users intend to express their retrieval intent through various query styles.…
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
Peng Jin, Hao Li, Zesen Cheng +5
Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local de…
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Peng Jin, Jinfa Huang, Pengfei Xiong +5
Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined…
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Kehan Li, Yian Zhao, Zhennan Wang +6
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and med…
Parallel Vertex Diffusion for Unified Visual Grounding
Zesen Cheng, Kehan Li, Peng Jin +4
Unified visual grounding pursues a simple and generic technical route to leverage multi-task data with less task-specific design. The most advanced methods typically present boxes…
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
Peng Jin, Hao Li, Zesen Cheng +5
Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, thi…
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Zesen Cheng, Pengchong Qiao, Kehan Li +6
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendo…
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Kehan Li, Zhennan Wang, Zesen Cheng +6
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the developmen…
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Kehan Li, Runyi Yu, Zhennan Wang +3
While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the…