few-shot classification
Cross Attention Network for Few-shot Classification
Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen
Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples.
Rethinking Generalization in Few-Shot Classification
Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly between training and test time. In this paper, we take a closer look at the implications in the context of few-shot learning. Splitting the input samples into patches and encoding these via the help of Vision Transformers allows us to establish semantic correspondences between local regions across images and independent of their respective class. The most informative patch embeddings for the task at hand are then determined as a function of the support set via online optimization at inference time, additionally providing visual interpretability of'what matters most' in the image. We build on recent advances in unsupervised training of networks via masked image modelling to overcome the lack of fine-grained labels and learn the more general statistical structure of the data while avoiding negative image-level annotation influence, aka supervision collapse. Experimental results show the competitiveness of our approach, achieving new state-of-the-art results on four popular few-shot classification benchmarks for 5-shot and 1-shot scenarios.
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Specifically, a set of prototypes is optimized to achieve per-task prototype overfit-ting, enabling accurately obtaining the overfitted prototypes for individual tasks. Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.
Adaptive Cross-Modal Few-shot Learning
Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O. O. Pinheiro
Neural Information Processing Systems http://nips.cc/
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
Neural Information Processing Systems http://nips.cc/
TowardsPracticalFew-ShotQuerySets: TransductiveMinimumDescriptionLengthInference
Inparticular,foreach task at testing time, theclasses effectivelypresent intheunlabeled query setareknown a priori, and correspond exactly to the set of classes represented in the labeled supportset.
Cross Attention Network for Few-shot Classification
Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen
Inthiswork,wepropose anovelCrossAttention Network to address the challenging problems in few-shot classification.