most citedLong-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model

3 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2026

Thinking Once Is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA

Zhongkuan Mao, Xianjie Liu, Tianyu Meng +9

High-resolution visual question answering (HR-VQA) is often treated as a problem of insufficient evidence acquisition, where failing multimodal large language models must inspect i…

cs.CV2026

Active Zero: Self-Evolving Vision-Language Models through Active Environment Exploration

Jinghan He, Junfeng Fang, Feng Xiong +5

Self-play has enabled large language models to autonomously improve through self-generated challenges. However, existing self-play methods for vision-language models rely on passiv…

cs.CV2026

Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models

Hao Tang, Yu Liu, Shuanglin Yan +3

Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in…

cs.CV2025

SceneDecorator: Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency

Quanjian Song, Donghao Zhou, Jingyu Lin +5

Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus o…

cs.CV2025

ASTRA: Let Arbitrary Subjects Transform in Video Editing

Fei Shen, Weihao Xu, Rui Yan +4

While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention dilution and mask boundary entang…

cs.CV2025

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup

Zhen Qu, Xian Tao, Xinyi Gong +7

Recent vision-language models (e.g., CLIP) have demonstrated remarkable class-generalizable ability to unseen classes in few-shot anomaly segmentation (FSAS), leveraging supervised…