# Probability Weighted Compact Feature for Domain Adaptive Retrieval

arXiv: https://arxiv.org/abs/2003.03293 · HTML: https://mlsanity.com/paper/2003.03293/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Dynamic Weighted Combiner for Mixed-Modal Image Retrieval](https://mlsanity.com/paper/2312.06179/) (2023)
- [G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2106.07853/) (2021)
- [Language Guided Local Infiltration for Interactive Image Retrieval](https://mlsanity.com/paper/2304.07747/) (2023)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos](https://mlsanity.com/paper/2112.08117/) (2021)
- [Fine-Grained AutoAugmentation for Multi-Label Classification](https://mlsanity.com/paper/2107.05384/) (2021)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)
- [Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval](https://mlsanity.com/paper/2505.13907/) (2025)
- [Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval](https://mlsanity.com/paper/2512.04524/) (2025)

## Abstract (arXiv)

Domain adaptive image retrieval includes single-domain retrieval and cross-domain retrieval. Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar. However, in practical application, the discrepancies between retrieval databases often taken in ideal illumination/pose/background/camera conditions and queries usually obtained in uncontrolled conditions are very large. In this paper, considering the practical application, we focus on challenging cross-domain retrieval. To address the problem, we propose an effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed. First, we derive our loss function through the Maximum A Posteriori Estimation (MAP): Bayesian Perspective (BP) induced focal-triplet loss, BP induced quantization loss and BP induced classification loss. Second, we propose a common manifold structure between domains to explore the potential correlation across domains. Considering the original feature representation is biased due to the inter-domain discrepancy, the manifold structure is difficult to be constructed. Therefore, we propose a new feature named Histogram Feature of Neighbors (HFON) from the sample statistics perspective. Extensive experiments on various benchmark databases validate that our method outperforms many state-of-the-art image retrieval methods for domain adaptive image retrieval. The source code is available at https://github.com/fuxianghuang1/PWCF