11 citations · 22 across the 13 of their papers we have counts for
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Activating the Discriminability of Novel Classes for Few-shot Segmentation
Dianwen Mei, Wei Zhuo, Jiandong Tian +2
Despite the remarkable success of existing methods for few-shot segmentation, there remain two crucial challenges. First, the feature learning for novel classes is suppressed durin…
Universal Object Detection with Large Vision Model
Feng Lin, Wenze Hu, Yaowei Wang +5
Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such systems have the potential to address a…
Semantic-Aware Local-Global Vision Transformer
Jiatong Zhang, Zengwei Yao, Fanglin Chen +2
Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks. It surmounts the ke…
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
Guimin Hu, Ting-En Lin, Yi Zhao +3
Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspect…
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
Fengjun Li, Xin Feng, Fanglin Chen +2
Typical methods for blind image super-resolution (SR) focus on dealing with unknown degradations by directly estimating them or learning the degradation representations in a latent…
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
Wenjie Pei, Shuang Wu, Dianwen Mei +3
While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specifi…
Learning Sequence Representations by Non-local Recurrent Neural Memory
Wenjie Pei, Xin Feng, Canmiao Fu +3
The key challenge of sequence representation learning is to capture the long-range temporal dependencies. Typical methods for supervised sequence representation learning are built…
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
Canran Li, Dongnan Liu, Haoran Li +4
Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models' adaptation ability in general computer vision. However, different from the natural im…
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Shuang Wu, Wenjie Pei, Dianwen Mei +3
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and…
Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition
Jingjing Wu, Pengyuan Lyu, Guangming Lu +2
Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions. De…
FaceMap: Towards Unsupervised Face Clustering via Map Equation
Xiaotian Yu, Yifan Yang, Aibo Wang +4
Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. The main challenge of this…