Learning Part Segmentation from Synthetic Animals (2311.18661v1)

Abstract: Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annotations impedes its usage across diverse object types. This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models to scale up existing synthetic data generated by computer-aided design (CAD) animal models. Compared to CAD models, SMAL models generate data with a wider range of poses observed in real-world scenarios. As a result, our first contribution is to construct a synthetic animal dataset of tigers and horses with more pose diversity, termed Synthetic Animal Parts (SAP). We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution. Concretely, we examine three Syn-to-Real adaptation methods but observe relative performance drop due to the innate difference between the two tasks. To address this, we propose a simple yet effective method called Class-Balanced Fourier Data Mixing (CB-FDM). Fourier Data Mixing aligns the spectral amplitudes of synthetic images with real images, thereby making the mixed images have more similar frequency content to real images. We further use Class-Balanced Pseudo-Label Re-Weighting to alleviate the imbalanced class distribution. We demonstrate the efficacy of CB-FDM on SynRealPart over previous methods with significant performance improvements. Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet, further underscoring the utility and potential applications of animal part segmentation.

• The paper introduces a Synthetic Animal Parts (SAP) dataset generated from SMAL models to overcome challenges of manual annotation.
• It demonstrates that the novel CB-FDM method, combining Fourier Data Mixing with class-balancing, significantly improves domain adaptation in part segmentation.
• The study shows that segmentation models trained on synthetic tigers and horses can effectively transfer to various quadruped species in real-world datasets.

Exploring Part Segmentation Using Synthetic Animal Data

The Challenge of Part Segmentation

Semantic part segmentation is a technique that enables a detailed understanding of an object by identifying its individual parts, which benefits numerous computer vision tasks. Despite its advantages, exhaustive manual annotation required for this task can be a significant obstacle when dealing with a variety of objects, notably animals. This is because real-world objects, such as animals, can have an extensive range of poses that are complex to capture and annotate accurately. Current datasets like PASCAL-Part and PartImageNet provide valuable annotations, but they are limited in their sample size and diversity, which restricts scalability to other animal species.

Pioneering Synthetic Animal Dataset

To tackle the limitations of manual annotation, the paper revolves around the generation of synthetic data using Skinned Multi-Animal Linear (SMAL) models that are known for efficient representation of animal shapes and poses. The synthesized data incorporates a variety of realistic poses, overcoming the limitations of traditional Computer-Aided Design (CAD) models that typically offer limited pose diversity. The researchers constructed a Synthetic Animal Parts (SAP) dataset encompassing tigers and horses with a wide range of poses, thus enriching the pose variability in the synthetic domain.

Domain Adaptation Methods and Their Enhancements

The paper established a Syn-to-Real benchmark called SynRealPart to enable the transfer of part segmentation learning from synthetic SAP data to real images from PartImageNet. Three state-of-the-art domain adaptation techniques that were initially designed for semantic segmentation were tested on this benchmark. However, the authors observed that these methods' performance declined when applied to part segmentation, which inspired the development of a novel technique named Class-Balanced Fourier Data Mixing (CB-FDM).

CB-FDM involves two key advancements. The first, Fourier Data Mixing (FDM), aligns spectral amplitudes between synthetic and real images before mixing them, leading to a closer resemblance in frequency content. The second, Class-Balanced Pseudo-Label Re-Weighting (CB), addresses class distribution imbalances in the SAP dataset. It applies greater emphasis on certain minority classes, particularly the animal head part, allowing the model to yield more balanced learning outcomes across different classes.

Transferability Across Species

One of the most notable findings from the research is the revelation that the segmented parts learned from synthetic tigers and horses can be transferred effectively to quadrupeds of various species in PartImageNet. This indicates the generalizability of the model and highlights its potential applications in broader contexts.

Conclusions and Future Directions

This research demonstrated the importance of pose diversity and synthetic data generation in enhancing the performance of semantic part segmentation. The introduction of the SAP dataset serves as a valuable resource for researchers in the field, and the CB-FDM method significantly improves the learning ability of domain adaptation models. The observed transferability across species can lead to efficient data construction strategies that focus on core animal sets, offering a solution to the limitations in available real-world data.

In summary, the work presented offers considerable advancements in animal part segmentation, setting the stage for future exploration in the field, although it also acknowledges some limitations in data variety and the treatment of new, unseen categories like animals with horns. It sets a promising direction for ongoing efforts to refine AI's visual perception of complex, real-world entities.