SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

TL;DR

This paper introduces SRCD, a novel framework for single-domain generalized object detection that leverages semantic reasoning and self-augmentation to improve model generalization across diverse target domains.

Contribution

The paper proposes SRCD, combining texture-based self-augmentation and local-global semantic reasoning to better model semantic structures in single-domain object detection.

Findings

SRCD outperforms existing methods on multiple benchmarks.

Semantic reasoning improves generalization to unseen domains.

Self-augmentation reduces attribute-related noise.

Abstract

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.