3 citations · 6 across the 7 of their papers we have counts for
7 papers
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…
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
Shanshan Zhong, Zhongzhan Huang, Daifeng Li +3
Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The inte…
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion
Jinghui Qin, Lihuang Fang, Ruitao Lu +2
Deep learning-based hyperspectral image (HSI) super-resolution, which aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral im…
Understanding Self-attention Mechanism via Dynamical System Perspective
Zhongzhan Huang, Mingfu Liang, Jinghui Qin +2
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models. However, current ex…
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
Shanshan Zhong, Wushao Wen, Jinghui Qin +2
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel region…