本文是 MIT CSAIL & Google Research 在2020年关于 Few-Shot Learning的又一篇力作,受 ICLR 2020 的经典文章 A baseline for few-shot image classification 启发,提出了如下假设: Embeddings are the most critical factor to the performance of few-shot learning/meta learning algorithms; better embeddings wi...
Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-Imagenet, Tiered-Imagenet, CIFAR-FS and FC-100 with the same ...
A Baseline for Few-Shot Image ClassificationGuneet Singh DhillonPratik ChaudhariAvinash RavichandranStefano SoattoInternational Conference on Learning Representations
Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. In: ICLR (2020) Google Scholar Dvornik, N., Schmid, C., Mairal, J.: Diversity with cooperation: ensemble methods for few-shot classification. In: ICCV (2019) Google Scholar ...
20200408 ICLR-20 A Baseline for Few-Shot Image Classification - A simple finetune+entropy minimization approach with strong baseline - 一个微调+最小化熵的小样本学习方法,结果很强 20200405 ICCV-19 Variational few-shot learning Variational few-shot learning 变分小样本学习 20200405 ICLR-20...
In this paper, a pairwise-based meta learning(PML) method is proposed for few-shot image classification. Transitive transfer learning is used to fine tune the pre-trained Resnet50 model to get a feature encoder that is more suitable for few shot task. Th
今天介绍一篇我们组和蚂蚁网商银行在小样本视频分类领域的工作 A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark,发表于BMVC 2021。现有的小样本视频分类方法往往采用元学习范式并且十分依赖ImageNet预训练,当不使用ImageNet预训练时,这些方法的性能下降严重。通过实验,我们发现这些方法在...
②当在最先进的CUB数据集上和mini-ImageNet上,以及一种评估few-shot分类算法跨域泛化能力的实验设置上做比较时,一个修改baseline 的方法令人惊讶的达到了竞争的表现。 ③结果显示,当特征backbone较浅时,减少这种类内变化是一个重要因素,当骨干网络较深时,则不明显。在一个真实的跨域评估装置中,作者展示了一个baseli...
In this short communication, we present a concise review of recent representative meta- learning methods for few-shot image classification. We re- fer to such methods as few-shot meta-learning methods. Af- ter establishing necessary notation, we first mathematically formulate few-shot learning and...
主要包括三个方面:常规目标识别(mini-ImageNet数据集)、细粒度分类(CUB-200-2011数据集)、跨域适应(mini-ImageNet →CUB)。 baseline++还是与sota可comparable 的。下面这个是few-shot分类精度与backbone深度的对比:很明显深度越深,不同方法性能的gap影响越小。