activity
20242026
most citedA Survey of Camouflaged Object Detection and Beyond

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation

Sujie Hu, Huaisong Zhang, Pujing Yang +7

Recent reinforcement learning approaches have improved visual text generation, yet generated images still suffer from incorrect wording and malformed glyphs. We identify two couple…

cs.CV2026

Embedding-perturbed Exploration Preference Optimization for Flow Models

Sujie Hu, Chubin Chen, Jiashu Zhu +3

Recent advancements have established Reinforcement Learning (RL) as a pivotal paradigm for aligning generative models with human intent. However, group-based optimization framework…

cs.CV2025

Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

Chubin Chen, Sujie Hu, Jiashu Zhu +8

Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, whil…

cs.CV2025

Uncertainty-Masked Bernoulli Diffusion for Camouflaged Object Detection Refinement

Yuqi Shen, Fengyang Xiao, Sujie Hu +5

Camouflaged Object Detection (COD) presents inherent challenges due to the subtle visual differences between targets and their backgrounds. While existing methods have made notable…

cs.CV2024★ 2 cited

A Survey of Camouflaged Object Detection and Beyond

Fengyang Xiao, Sujie Hu, Yuqi Shen +6

Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for comput…

cs.CV2024

TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes

Bu Jin, Yupeng Zheng, Pengfei Li +12

3D dense captioning stands as a cornerstone in achieving a comprehensive understanding of 3D scenes through natural language. It has recently witnessed remarkable achievements, par…