163 citations · 489 across the 48 of their papers we have counts for
48 papers
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…
Learning Physics from Pretrained Video Models: A Multimodal Continuous and Sequential World Interaction Models for Robotic Manipulation
Zijian Song, Qichang Li, Sihan Qin +4
The scarcity of large-scale robotic data has motivated the repurposing of foundation models from other modalities for policy learning. In this work, we introduce PhysGen (Learning…
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…
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…
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…
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…
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…
Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labels
Haoxian Ruan, Zhihua Xu, Zhijing Yang +3
Multi-label recognition with partial labels (MLR-PL), in which only some labels are known while others are unknown for each image, is a practical task in computer vision, since col…
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
Tianshui Chen, Weihang Wang, Tao Pu +4
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confiden…
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior
Yukai Shi, Yupei Lin, Pengxu Wei +3
Recently, researchers have proposed various deep learning methods to accurately detect infrared targets with the characteristics of indistinct shape and texture. Due to the limited…
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition
Yuefang Gao, Yuhao Xie, Zeke Zexi Hu +2
Domain shift poses a significant challenge in Cross-Domain Facial Expression Recognition (CD-FER) due to the distribution variation across different domains. Current works mainly f…
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
Hefeng Wu, Weifeng Chen, Zhibin Liu +3
Given a descriptive text query, text-based person search (TBPS) aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite ch…
SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting
Hefeng Wu, Yandong Chen, Lingbo Liu +3
The class-agnostic counting (CAC) task has recently been proposed to solve the problem of counting all objects of an arbitrary class with several exemplars given in the input image…
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
Tao Pu, Tianshui Chen, Hefeng Wu +2
Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding…
RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs
Zhouxia Wang, Jiawei Zhang, Tianshui Chen +2
Blind face restoration aims at recovering high-quality face images from those with unknown degradations. Current algorithms mainly introduce priors to complement high-quality detai…
Perception and Semantic Aware Regularization for Sequential Confidence Calibration
Zhenghua Peng, Yu Luo, Tianshui Chen +2
Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as su…
Open-World Pose Transfer via Sequential Test-Time Adaption
Junyang Chen, Xiaoyu Xian, Zhijing Yang +5
Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representat…
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup
Zhijing Yang, Junyang Chen, Yukai Shi +3
Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and comput…
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels
Tao Pu, Qianru Lao, Hefeng Wu +2
As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other a…
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +3
Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-s…
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Tianshui Chen, Tao Pu, Lingbo Liu +3
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and th…
Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation
Yupei Lin, Sen Zhang, Tianshui Chen +3
Unpaired image-to-image translation aims to find a mapping between the source domain and the target domain. To alleviate the problem of the lack of supervised labels for the source…
Semantic Representation and Dependency Learning for Multi-Label Image Recognition
Tao Pu, Mingzhan Sun, Hefeng Wu +3
Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing…
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +1
Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging…