The human visual system has the remarkably ability to be able to effortlessly
learn novel concepts from only a few examples. Mimicking the same behavior on
machine learning vision systems is an interesting and very challenging research
problem with many practical advantages on real world vision applications. In
this context, the goal of our work is to devise a few-shot visual learning
system that during test time it will be able to efficiently learn novel
categories from only a few training data while at the same time it will not
forget the initial categories on which it was trained (here called base
categories). To achieve that goal we propose (a) to extend an object
recognition system with an attention based few-shot classification weight
generator, and (b) to redesign the classifier of a ConvNet model as the cosine
similarity function between feature representations and classification weight
vectors. The latter, apart from unifying the recognition of both novel and base
categories, it also leads to feature representations that generalize better on
"unseen" categories. We extensively evaluate our approach on Mini-ImageNet
where we manage to improve the prior state-of-the-art on few-shot recognition
(i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings
respectively) while at the same time we do not sacrifice any accuracy on the
base categories, which is a characteristic that most prior approaches lack.
Finally, we apply our approach on the recently introduced few-shot benchmark of
Bharath and Girshick [4] where we also achieve state-of-the-art results. The
code and models of our paper will be published on:
this https URL

该研究提出了一种基于卷积神经网络和注意力机制的少样本视觉学习系统，旨在实现在测试时只使用少量训练数据就能有效地学习新类别，同时不会忘记原先基础类别的模型。经过在 Mini-ImageNet 数据集的测试，该模型在 1-shot 和 5-shot 设置下都取得了 56.20% 和 73.00% 的最优成绩。

无遗忘动态少样本视觉学习

Dynamic Few-Shot Visual Learning without Forgetting

We propose a novel ConvNet model for predicting 2D human body poses in an
image. The model regresses a heatmap representation for each body keypoint, and
is able to learn and represent both the part appearances and the context of the
part configuration. We make the following three contributions: (i) an
architecture combining a feed forward module with a recurrent module, where the
recurrent module can be run iteratively to improve the performance, (ii) the
model can be trained end-to-end and from scratch, with auxiliary losses
incorporated to improve performance, (iii) we investigate whether keypoint
visibility can also be predicted. The model is evaluated on two benchmark
datasets. The result is a simple architecture that achieves performance on par
with the state of the art, but without the complexity of a graphical model
stage (or layers).

本文提出了一种新颖的 ConvNet 模型，可预测图像中的 2D 人体姿势，通过回归每个关键点的热度图表示，并能够学习和表示部件外观和配置上下文。模型可以从头开始和端到端训练，用于改善性能的辅助损失。该模型在两个基准数据集上进行了评估，具有与最先进技术相当的性能，但不含有图形模型阶段（或层）的复杂性。