Few-shot object classification is the task of classifying objects in an image
with limited number of examples as supervision. We propose a one-shot/few-shot
classification model that can classify an object of any unseen class into a
relatively general category in an hierarchically based classification. Our
model uses a three-level hierarchical contrastive loss based ResNet152
classifier for classifying an object based on its features extracted from Image
embedding, not used during the training phase. For our experimentation, we have
used a subset of the ImageNet (ILSVRC-12) dataset that contains only the animal
classes for training our model and created our own dataset of unseen classes
for evaluating our trained model. Our model provides satisfactory results in
classifying the unknown objects into a generic category which has been later
discussed in greater detail.

在有限的样本标注下，通过图像对目标对象进行少样本分类是一个任务。我们提出了一个一次性 / 少次分类模型，可以将任何未知类别的对象分类到一个相对通用的类别中，采用了基于层次对比损失的 ResNet152 分类器，基于图像嵌入所提取的特征对对象进行分类，而这些特征在训练阶段未被使用。我们在 ImageNet（ILSVRC-12）数据集的子集上进行了实验，该子集只包含用于训练我们的模型的动物类别，并创建了一组我们自己的未知类别数据集，用于评估我们训练得到的模型。我们的模型在将未知对象分类为通用类别方面得到了令人满意的结果，并对此进行了更详细的讨论。

CHIP：对比度层次图像预训练

CHIP: Contrastive Hierarchical Image Pretraining

As a promising scheme of self-supervised learning, masked autoencoding has
significantly advanced natural language processing and computer vision.
Inspired by this, we propose a neat scheme of masked autoencoders for point
cloud self-supervised learning, addressing the challenges posed by point
cloud's properties, including leakage of location information and uneven
information density. Concretely, we divide the input point cloud into irregular
point patches and randomly mask them at a high ratio. Then, a standard
Transformer based autoencoder, with an asymmetric design and a shifting mask
tokens operation, learns high-level latent features from unmasked point
patches, aiming to reconstruct the masked point patches. Extensive experiments
show that our approach is efficient during pre-training and generalizes well on
various downstream tasks. Specifically, our pre-trained models achieve 85.18%
accuracy on ScanObjectNN and 94.04% accuracy on ModelNet40, outperforming all
the other self-supervised learning methods. We show with our scheme, a simple
architecture entirely based on standard Transformers can surpass dedicated
Transformer models from supervised learning. Our approach also advances
state-of-the-art accuracies by 1.5%-2.3% in the few-shot object classification.
Furthermore, our work inspires the feasibility of applying unified
architectures from languages and images to the point cloud.

本文提出一种新颖的掩码自编码点云自监督学习方案，通过划分输入点云为不规则点区块和高比例随机掩码，设计了一种基于 Transformer 的自编码器，该方案在预训练过程中高效并且在各种下游任务中具有很好的泛化能力，包括在少样本物体分类方面表现出色。