The field of visual few-shot classification aims at transferring the state-of-the-art performance of deep learning visual systems onto tasks where only a very limited number of training samples are available. The main solution consists in training a feature extractor using a large and diverse dataset to be applied to the considered few-shot task. Thanks to the encoded priors in the feature extractors, classification tasks with as little as one example (or "shot'') for each class can be solved with high accuracy, even when the shots display individual features not representative of their classes. Yet, the problem becomes more complicated when some of the given shots display multiple objects. In this paper, we present a strategy which aims at detecting the presence of multiple and previously unseen objects in a given shot. This methodology is based on identifying the corners of a simplex in a high dimensional space. We introduce an optimization routine and showcase its ability to successfully detect multiple (previously unseen) objects in raw images. Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class. Using standard benchmarks of the field, we show the ability of the proposed method to slightly, yet statistically significantly, improve accuracy in these settings.
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Beihang University, Beijing 100191, China（北航，北京100191，中国）；Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China（航天信息研究所，中国科学院，北京100190，中国）
Dynamic Visual-semantic Alignment for Zero-shot Learning with Ambiguous Labels
College of Computer Science and Technology（计算机科学与技术学院）；School of Computer Science（计算机科学学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education（教育部计算能力网络与信息安全重点实验室）；Shandong Computer Science Center（山东计算机科学中心）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））
Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（Inria巴黎-鲁克蒙特研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒实验室）
