Learning Attributes Equals Multi-Source Domain Generalization (1605.00743v1)

Abstract: Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem---how to accurately and robustly detect attributes from images---has been left under explored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multi-source domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and three different problems.

• The paper proposes reframing attribute detection as a multi-source domain generalization problem to enhance generalizability across unseen categories.
• It integrates UDICA and centered kernel alignment to improve feature representation while reducing inter-domain variance in visual attributes.
• Extensive experiments on diverse datasets demonstrate significant gains in attribute detection, zero-shot learning, and image retrieval tasks.

Learning Attributes Equals Multi-Source Domain Generalization

The paper "Learning Attributes Equals Multi-Source Domain Generalization" proposes a novel approach to tackle the fundamental problem of attribute detection by framing it as a multi-source domain generalization issue. The authors detail the intrinsic appeal of visual attributes, which serve as mid-level descriptors used by humans to classify objects, scenes, and activities, and underscore the need for attribute detectors to generalize effectively across seen and unseen categories.

Core Contributions

The key contributions of this paper are threefold:

1. Domain Generalization for Attribute Detection: The authors propose viewing attribute detection through the lens of domain generalization. They suggest treating each category as a separate domain and leveraging multi-source domain generalization techniques to enhance the cross-category generalization capabilities of attribute detectors. This novel perspective aims to address the inadequacies in existing attribute detection methods concerning generalizing across categories, especially those that haven't been seen during training.
2. Integration of UDICA and Centered Kernel Alignment: The paper introduces an integration of Unsupervised Domain-Invariant Component Analysis (UDICA) and centered kernel alignment to develop a new feature representation. UDICA minimizes distributional variance among categories while maximizing data variance, thus enhancing the ability of detectors to operate across diverse categories. Centered kernel alignment strengthens the discriminative power of the features by aligning them with attribute label-based kernel matrices.
3. Empirical Validation Across Diverse Datasets: Extensive experiments are conducted on the Animal With Attributes, Caltech-UCSD Birds, aPascal-aYahoo, and UCF101 datasets to demonstrate the effectiveness of the proposed approach. The results show significant improvements over baseline methods in attribute detection, zero-shot learning, and multi-attribute-based image retrieval tasks. These empirical validations highlight the practical benefits of adopting a domain generalization framework for learning attributes.

Implications and Future Prospects

The implications of this research extend both theoretically and practically:

• Theory: From a theoretical standpoint, the paper expands the understanding of attribute detection by linking it to domain generalization. This connection calls for further explorations into other generalization methods and feature representations to refine attribute detectors' precision and robustness further.
• Applications: Practically, the improved generalization capability enables more reliable use of attributes in various high-level tasks like zero-shot learning and semantic search, where the objects to be identified may not have been encountered beforehand. This enhancement broadens the applicability of computer vision systems in real-world scenarios.
• Future Work: The research opens avenues for future work to explore joint distributions of inputs and attributes rather than focusing solely on marginal distributions, which could provide deeper insights into the paired learning of object categories and their descriptive attributes.

In conclusion, the paper presents a well-founded approach to refining attribute detectors using domain generalization techniques, validated by robust experimental results across diverse datasets and vision tasks.