35 citations · 154 across the 26 of their papers we have counts for
12 papers · 1 filter
Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting
Junwu Zhang, Zhenyu Tang, Yatian Pang +5
Recent one image to 3D generation methods commonly adopt Score Distillation Sampling (SDS). Despite the impressive results, there are multiple deficiencies including multi-view inc…
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.…
Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs
Peng Jin, Yang Wu, Yanbo Fan +3
Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicit…
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
Bin Lin, Yang Ye, Bin Zhu +4
The Large Vision-Language Model (LVLM) has enhanced the performance of various downstream tasks in visual-language understanding. Most existing approaches encode images and videos…
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Peng Jin, Ryuichi Takanobu, Wancai Zhang +2
Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversati…
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Zesen Cheng, Peng Jin, Hao Li +5
The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by…
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…
TG-VQA: Ternary Game of Video Question Answering
Hao Li, Peng Jin, Zesen Cheng +5
Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them. However, since relying heavily on human instructions…
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…