PartImageNet: A Large, High-Quality Dataset of Parts (2112.00933v3)

Abstract: It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also help for downstream tasks like activity recognition. Research on part-based models, however, is hindered by the lack of datasets with per-pixel part annotations. This is partly due to the difficulty and high cost of annotating object parts so it has rarely been done except for humans (where there exists a big literature on part-based models). To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations. It consists of $158$ classes from ImageNet with approximately $24,000$ images. PartImageNet is unique because it offers part-level annotations on a general set of classes including non-rigid, articulated objects, while having an order of magnitude larger size compared to existing part datasets (excluding datasets of humans). It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. We conduct comprehensive experiments which study these tasks and set up a set of baselines. The dataset and scripts are released at https://github.com/TACJu/PartImageNet.

• The paper presents a novel dataset featuring 24,000 images with per-pixel part annotations across 158 classes.
• The robust annotation pipeline ensures accurate segmentation for both rigid and non-rigid, articulated objects.
• The dataset drives improvements in semantic part segmentation, few-shot learning, and object recognition tasks.

An Expert Analysis of "PartImageNet: A Large, High-Quality Dataset of Parts"

The paper "PartImageNet: A Large, High-Quality Dataset of Parts" introduces a dataset aimed at advancing research in part-based models within the field of computer vision. The dataset, derived from ImageNet, encompasses 158 classes with around 24,000 images. Its significant contribution lies in providing high-quality, per-pixel part segmentation annotations across a broad set of classes, overcoming existing limitations in datasets focused on specific categories such as humans or rigid objects.

Dataset Composition and Design

PartImageNet distinguishes itself by offering annotations for a wide range of classes, notably including non-rigid and articulated objects. The dataset follows a hierarchical structure where classes are grouped into 11 super-categories, each assigned specific part labels. This organization facilitates multi-level analysis, from super-category to fine-grained class levels. With approximately 158 classes represented, the dataset is substantially larger and more diversified compared to previous efforts like PASCAL-Part.

Methodology for Annotation

The dataset has been curated using a meticulous annotation pipeline to ensure consistency and accuracy. This involves a tiered approach where images are filtered for suitability, annotated by trained individuals, and subsequently reviewed by inspectors and examiners. This rigorous process addresses challenges associated with part segmentation, such as occlusion and distinguishing boundaries between parts.

Potential Applications

The introduction of PartImageNet is anticipated to significantly aid various tasks in computer vision:

• Semantic Part Segmentation: The dataset provides a basis for developing models that can segment parts of objects, leading to potential improvements in semantic segmentation tasks.
• Few-Shot Learning: By providing part annotations, the dataset could enhance the efficacy of models in scenarios with limited examples, enabling better generalization across object classes.
• Object Segmentation: The dataset's detailed part annotations may facilitate improved performance in object segmentation tasks by providing intermediate supervision at the part level.

Experimental Evaluation

The authors conducted extensive experiments utilizing a range of methodologies, including Semantic FPN, DeepLabv3+, and SegFormer, which serve as baselines for part segmentation. The results indicate that while existing methods yield reasonable performance, incorporating part-level annotations as supervisory signals offers additional improvements, especially in object segmentation and few-shot learning tasks.

Implications and Future Directions

PartImageNet stands to bolster research in part-based models by addressing the dearth of annotated data in this area. Its implications extend to the improvement of interpretability and accuracy in object recognition and segmentation algorithms.

Looking ahead, the dataset opens avenues for exploring how part-level recognition can be integrated with other machine learning paradigms to enhance robustness and generalization. Specifically, future research might focus on innovative model architectures that leverage part annotations more effectively, or investigating unsupervised learning models that emulate the human ability to discern object parts without explicit supervision.

In summary, the introduction of PartImageNet provides a critical resource for advancing part-based modeling in computer vision, with extensive potential for enriching both theoretical research and practical applications in AI.