Beyond Frequency: Seeing Subtle Cues Through the Lens of Spatial Decomposition for Fine-Grained Visual Classification

TL;DR

This paper introduces SCOPE, a novel adaptive spatial decomposition method that enhances fine-grained visual classification by dynamically capturing subtle details and semantic features, surpassing fixed frequency-based approaches.

Contribution

The paper proposes SCOPE, a new adaptive spatial decomposition framework with modules for detail enhancement and semantic refinement, improving flexibility over traditional frequency domain methods.

Findings

Achieves state-of-the-art results on four FGVC benchmarks.

Effectively captures subtle visual cues and semantic information.

Outperforms fixed basis frequency methods.

Abstract

The crux of resolving fine-grained visual classification (FGVC) lies in capturing discriminative and class-specific cues that correspond to subtle visual characteristics. Recently, frequency decomposition/transform based approaches have attracted considerable interests since its appearing discriminative cue mining ability. However, the frequency-domain methods are based on fixed basis functions, lacking adaptability to image content and unable to dynamically adjust feature extraction according to the discriminative requirements of different images. To address this, we propose a novel method for FGVC, named Subtle-Cue Oriented Perception Engine (SCOPE), which adaptively enhances the representational capability of low-level details and high-level semantics in the spatial domain, breaking through the limitations of fixed scales in the frequency domain and improving the flexibility of multi-scale fusion. The core of SCOPE lies in two modules: the Subtle Detail Extractor (SDE), which dynamically enhances subtle details such as edges and textures from shallow features, and the Salient Semantic Refiner (SSR), which learns semantically coherent and structure-aware refinement features from the high-level features guided by the enhanced shallow features. The SDE and SSR are cascaded stage-by-stage to progressively combine local details with global semantics. Extensive experiments demonstrate that our method achieves new state-of-the-art on four popular fine-grained image classification benchmarks.