works on

From the 1 of 11 linked papers with an AI index.

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2026

Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation

Xinshun Wang, Peiming Li, Ziyi Wang +5

Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vision. However, current paradigms…

cs.CV2026

Towards One-to-Many Temporal Grounding

Qi Xu, Yue Tan, Shihao Chen +5

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, ho…

cs.CV2026

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models

Yueyi Sun, Yuhao Wang, Jason Li +8

Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limi…

cs.CV2026

AstroRAG -- A Pagerank-Based Retrieval-Augmented Generation Pipeline for Question Answering in Astronomy

Zhifeng Wang, Jason Jingshi Li, Kaihao Zhang +1

Large language models (LLMs) demonstrate strong performance in natural language processing but often generate factual errors when relying solely on parametric knowledge. Retrieval-…

cs.CV2026

Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation

Junjie Wang, Xinghua Lou, Jason Li +8

Text-to-Image (T2I) models and Unified Multimodal Models (UMMs) have achieved remarkable progress in visual generation. However, their reliance on a single-pass generation paradigm…

cs.CV2026

Dynamic-TreeRPO: Breaking the Independent Trajectory Bottleneck with Structured Sampling

Xiaolong Fu, Lichen Ma, Zipeng Guo +9

The integration of Reinforcement Learning (RL) into flow matching models for text-to-image (T2I) generation has driven substantial advances in generation quality. However, these ga…