9 citations · 29 across the 10 of their papers we have counts for
10 papers
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)
Ya-nan Guan, Shaonan Zhang, Hang Guo +55
In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite sign…
PersonificationNet: Making customized subject act like a person
Tianchu Guo, Pengyu Li, Biao Wang +1
Recently customized generation has significant potential, which uses as few as 3-5 user-provided images to train a model to synthesize new images of a specified subject. Though sub…
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Kaicheng Li, Hongyu Yang, Binghui Chen +3
Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack t…
Generative Action Description Prompts for Skeleton-based Action Recognition
Wangmeng Xiang, Chao Li, Yuxuan Zhou +2
Skeleton-based action recognition has recently received considerable attention. Current approaches to skeleton-based action recognition are typically formulated as one-hot classifi…
Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition
Wangmeng Xiang, Chao Li, Biao Wang +3
Transformer-based methods have recently achieved great advancement on 2D image-based vision tasks. For 3D video-based tasks such as action recognition, however, directly applying s…
SP-ViT: Learning 2D Spatial Priors for Vision Transformers
Yuxuan Zhou, Wangmeng Xiang, Chao Li +5
Recently, transformers have shown great potential in image classification and established state-of-the-art results on the ImageNet benchmark. However, compared to CNNs, transformer…
Dense Learning based Semi-Supervised Object Detection
Binghui Chen, Pengyu Li, Xiang Chen +3
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data. Though various self-tr…
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
Binghui Chen, Zhaoyi Yan, Ke Li +4
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity…
Continual Local Replacement for Few-shot Learning
Canyu Le, Zhonggui Chen, Xihan Wei +2
The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good…
Learning Continually from Low-shot Data Stream
Canyu Le, Xihan Wei, Biao Wang +2
While deep learning has achieved remarkable results on various applications, it is usually data hungry and struggles to learn over non-stationary data stream. To solve these two li…