5 papers
CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning
Zhijiang Tang, Linhua Wang, Jiaxin Qi +4
Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references. Because human…
Siamese Transformer Networks for Few-shot Image Classification
Weihao Jiang, Shuoxi Zhang, Kun He
Humans exhibit remarkable proficiency in visual classification tasks, accurately recognizing and classifying new images with minimal examples. This ability is attributed to their c…
Class-relevant Patch Embedding Selection for Few-Shot Image Classification
Weihao Jiang, Haoyang Cui, Kun He
Effective image classification hinges on discerning relevant features from both foreground and background elements, with the foreground typically holding the critical information.…
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning
Weihao Jiang, Chang Liu, Kun He
Humans possess remarkable ability to accurately classify new, unseen images after being exposed to only a few examples. Such ability stems from their capacity to identify common fe…
Boosting Meta-Training with Base Class Information for Few-Shot Learning
Weihao Jiang, Guodong Liu, Di He +1
Few-shot learning, a challenging task in machine learning, aims to learn a classifier adaptable to recognize new, unseen classes with limited labeled examples. Meta-learning has em…