11 citations · 22 across the 13 of their papers we have counts for
14 papers
Restoring without Forgetting: Filter-Level Continual Image Restoration via Parameter-Space Integrated Gradients
Xin Feng, Jin Zhao, Yizhen Zhang +3
Adapting image restoration models to a stream of new tasks without revisiting past data remains challenging due to catastrophic forgetting. In this work, we propose Restoring witho…
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
Zhenhua Ning, Zhuotao Tian, Guangming Lu +1
Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper pr…
Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction
Guimin Hu, Yi Zhao, Guangming Lu
Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotio…
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…