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20162026
most citedMulti-label Image Recognition by Recurrently Discovering Attentional Regions

53 citations · 201 across the 26 of their papers we have counts for

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37 papers · 1 filter

cs.CV2026

Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation

Tianshui Chen, Yujie Zhu, Jianman Lin +4

Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original speech. A primary challenge in t…

cs.CV2025

In-Situ Tweedie Discrete Diffusion Models

Xiao Li, Jiaqi Zhang, Shuxiang Zhang +3

While diffusion models excel at generating continuous data such as images, adapting them to discrete tasks has relied on indirect approaches that either operate in continuous embed…

cs.CV2025

Physical Autoregressive Model for Robotic Manipulation without Action Pretraining

Zijian Song, Sihan Qin, Tianshui Chen +2

The scarcity of manipulation data has motivated the use of pretrained large models from other modalities in robotics. In this work, we build upon autoregressive video generation mo…

cs.CV2025

Neural Scene Designer: Self-Styled Semantic Image Manipulation

Jianman Lin, Tianshui Chen, Chunmei Qing +4

Maintaining stylistic consistency is crucial for the cohesion and aesthetic appeal of images, a fundamental requirement in effective image editing and inpainting. However, existing…

cs.CV2025

Geometry-Editable and Appearance-Preserving Object Compositon

Jianman Lin, Haojie Li, Chunmei Qing +3

General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simultaneously preserving its fine-gr…

cs.CV2025

Exploiting Temporal Audio-Visual Correlation Embedding for Audio-Driven One-Shot Talking Head Animation

Zhihua Xu, Tianshui Chen, Zhijing Yang +3

The paramount challenge in audio-driven One-shot Talking Head Animation (ADOS-THA) lies in capturing subtle imperceptible changes between adjacent video frames. Inherently, the tem…