Novel category discovery aims at adapting models trained on known categories
to novel categories. Previous works only focus on the scenario where known and
novel categories are of the same granularity. In this paper, we investigate a
new practical scenario called Fine-grained Category Discovery under
Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained
categories with only coarse-grained labeled data, which can adapt models to
categories of different granularity from known ones and reduce significant
labeling cost. It is also a challenging task since supervised training on
coarse-grained categories tends to focus on inter-class distance (distance
between coarse-grained classes) but ignore intra-class distance (distance
between fine-grained sub-classes) which is essential for separating
fine-grained categories. Considering most current methods cannot transfer
knowledge from coarse-grained level to fine-grained level, we propose a
hierarchical weighted self-contrastive network by building a novel weighted
self-contrastive module and combining it with supervised learning in a
hierarchical manner. Extensive experiments on public datasets show both
effectiveness and efficiency of our model over compared methods. Code and data
are available at this https URL.

本研究提出了一种新的细粒度类别发现的场景，称为基于粗糙标注的细粒度类别发现 (FCDC)，它旨在通过仅使用粗糙标记数据发现细粒度类别，可以适应来自已知类别的不同粒度的类别并降低标记成本，而我们的方法则通过结合自对比学习和有监督学习进行层次加权处理，具有良好的效果和效率。

基于层次化加权自对比学习的精细类别发现在粗粒度监督下的应用

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning

In order for artificial agents to successfully perform tasks in changing
environments, they must be able to both detect and adapt to novelty. However,
visual novelty detection research often only evaluates on repurposed datasets
such as CIFAR-10 originally intended for object classification, where images
focus on one distinct, well-centered object. New benchmarks are needed to
represent the challenges of navigating the complex scenes of an open world. Our
new NovelCraft dataset contains multimodal episodic data of the images and
symbolic world-states seen by an agent completing a pogo stick assembly task
within a modified Minecraft environment. In some episodes, we insert novel
objects within the complex 3D scene that may impact gameplay and appear in a
variety of sizes and positions. Our visual novelty detection benchmark finds
that methods that rank best on popular area-under-the-curve metrics may be
outperformed by simpler alternatives when controlling false positives matters
most. Further multi-modal novelty detection experiments suggest that methods
that fuse both visual and symbolic information can improve time until detection
as well as overall discrimination. Finally, our evaluation of recent
generalized category discovery methods suggests that adapting to new imbalanced
categories in complex scenes remains an exciting open problem.

本研究介绍了一个新的基准数据集 NovelCraft，包含在 Minecraft 环境中完成跳跃球组装任务的代理人所看到的图像和符号世界状态的多模态故事数据。进一步的多模态新颖性检测实验表明，融合视觉和象征信息的方法能够提高检测时间和总体识别能力，同时对于复杂场景中新不平衡分类的适应仍然是一个令人兴奋的开放性问题。