PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval

TL;DR

PHPQ introduces a pyramid hybrid pooling and learnable quantization approach to enhance fine-grained image retrieval by capturing subtle details and optimizing codebook relevance, outperforming existing methods.

Contribution

The paper proposes a novel Pyramid Hybrid Pooling module and a learnable quantization mechanism to improve fine-grained image hashing accuracy.

Findings

PHPQ outperforms state-of-the-art methods on CUB-200-2011 and Stanford Dogs datasets.

The pyramid pooling captures multi-level features for better discrimination.

The learnable quantization improves codebook relevance and quantization quality.

Abstract

Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to their high computation and storage efficiency. Most existing hashing methods cannot produce satisfactory results for fine-grained retrieval, because they usually adopt the outputs of the last CNN layer to generate binary codes. Since deeper layers tend to summarize visual clues, e.g., texture, into abstract semantics, e.g., dogs and cats, the feature produced by the last CNN layer is less effective in capturing subtle but discriminative visual details that mostly exist in shallow layers. To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ). Specifically, we propose a Pyramid Hybrid Pooling (PHP) module to capture and preserve fine-grained semantic information from multi-level features, which emphasizes the subtle discrimination of different sub-categories. Besides, we propose a learnable quantization module with a partial codebook attention mechanism, which helps to optimize the most relevant codewords and improves the quantization. Comprehensive experiments on two widely-used public benchmarks, i.e., CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.