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.

翻译：摘要：少样本目标分类是指在监督样本数量有限的情况下对图像中目标进行分类的任务。我们提出了一种单样本/少样本分类模型，该模型能够基于层次分类结构，将任意未见类别中的目标归类到相对通用的类别中。本模型采用基于三级层次对比损失函数训练的ResNet152分类器，通过提取图像嵌入中的特征（该特征在训练阶段未被使用）对目标进行分类。实验过程中，我们使用了ImageNet（ILSVRC-12）数据集中仅包含动物类别的子集进行模型训练，并自主构建了包含未见类别的数据集以评估训练后的模型。实验结果表明，本模型在将未知目标归类至通用类别方面取得了令人满意的效果，后续章节将对此进行详细讨论。