Weighted Contrastive Hashing

• URL: http://arxiv.org/abs/2209.14099v1
• Date: Wed, 28 Sep 2022 13:47:33 GMT
• Title: Weighted Contrastive Hashing
• Authors: Jiaguo Yu, Huming Qiu, Dubing Chen, Haofeng Zhang
• Abstract summary: Unsupervised hash development has been hampered by insufficient data similarity mining based on global-only image representations. We introduce a novel mutual attention module to alleviate the problem of information asymmetry in network features caused by the missing image structure. The aggregated weighted similarities, which reflect the deep image relations, are distilled to facilitate the hash codes learning with a distillation loss.
• Score: 11.14153532458873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of unsupervised hashing is advanced by the recent popular contrastive learning paradigm. However, previous contrastive learning-based works have been hampered by (1) insufficient data similarity mining based on global-only image representations, and (2) the hash code semantic loss caused by the data augmentation. In this paper, we propose a novel method, namely Weighted Contrative Hashing (WCH), to take a step towards solving these two problems. We introduce a novel mutual attention module to alleviate the problem of information asymmetry in network features caused by the missing image structure during contrative augmentation. Furthermore, we explore the fine-grained semantic relations between images, i.e., we divide the images into multiple patches and calculate similarities between patches. The aggregated weighted similarities, which reflect the deep image relations, are distilled to facilitate the hash codes learning with a distillation loss, so as to obtain better retrieval performance. Extensive experiments show that the proposed WCH significantly outperforms existing unsupervised hashing methods on three benchmark datasets.

Related papers

• Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing [71.47723696190184]
  We propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method for semantic hashing. BRCD is specifically devised for the distillation of semantic hashing models.
  arXiv  Detail & Related papers  (2024-03-10T03:33:59Z)
• PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation [50.556961575275345]
  We propose a perception-aware fusion framework to promote segmentation robustness in adversarial scenes. We show that our scheme substantially enhances the robustness, with gains of 15.3% mIOU, compared with advanced competitors.
  arXiv  Detail & Related papers  (2023-08-08T01:55:44Z)
• Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashing [1.6244541005112747]
  Fine-grained hashing is more challenging than traditional hashing problems. We propose a cascaded network to learn compact and highly semantic hash codes. We also propose a novel approach to coordinately balance the loss of multi-task learning.
  arXiv  Detail & Related papers  (2023-03-20T17:08:48Z)
• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• Deep Self-Adaptive Hashing for Image Retrieval [16.768754022585057]
  We propose a textbfDeep Self-Adaptive Hashing(DSAH) model to adaptively capture the semantic information with two special designs. First, we construct a neighborhood-based similarity matrix, and then refine this initial similarity matrix with a novel update strategy. Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning.
  arXiv  Detail & Related papers  (2021-08-16T13:53:20Z)
• CIMON: Towards High-quality Hash Codes [63.37321228830102]
  We propose a new method named textbfComprehensive stextbfImilarity textbfMining and ctextbfOnsistency leartextbfNing (CIMON) First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes.
  arXiv  Detail & Related papers  (2020-10-15T14:47:14Z)
• Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator [62.26981903551382]
  Variational auto-encoders (VAEs) with binary latent variables provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing. This new semantic hashing framework achieves superior performance compared to the state-of-the-arts.
  arXiv  Detail & Related papers  (2020-05-21T06:11:33Z)
• Learning to hash with semantic similarity metrics and empirical KL divergence [3.04585143845864]
  Learning to hash is an efficient paradigm for exact and approximate nearest neighbor search from massive databases. Binary hash codes are typically extracted from an image by rounding output features from a CNN, which is trained on a supervised binary similar/ dissimilar task. We overcome (i) via a novel loss function encouraging the relative hash code distances of learned features to match those derived from their targets. We address (ii) via a differentiable estimate of the KL divergence between network outputs and a binary target distribution, resulting in minimal information loss when the features are rounded to binary.
  arXiv  Detail & Related papers  (2020-05-11T08:20:26Z)
• Reinforcing Short-Length Hashing [61.75883795807109]
  Existing methods have poor performance in retrieval using an extremely short-length hash code. In this study, we propose a novel reinforcing short-length hashing (RSLH) In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information. Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
  arXiv  Detail & Related papers  (2020-04-24T02:23:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.