{"ID":5935738,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03328","arxiv_id":"2607.03328","title":"Beyond Post-Quantization: Native Hash Learning with a Dedicated HASH Token","abstract":"Efficient large-scale image retrieval requires compact representations that preserve semantic similarity under fast Hamming-space search. Deep hashing is appealing, but most existing CNN- and ViT-based methods still follow a post-quantization paradigm, where continuous visual features are first learned and binary codes are then produced by a terminal hash projection or binarization operation. This late code generation creates a feature-to-code discrepancy between the continuously optimized representation space and the discrete Hamming space used for retrieval. To address this limitation, we propose HashViT, a Vision Transformer framework for native hash token learning. Instead of treating hashing as a terminal readout, HashViT introduces a dedicated HASH token that serves as a persistent, hash-oriented retrieval state inside the transformer. The HASH token is structurally decomposed into a Hash Register for direct binary code generation and a Semantic Workspace for preserving auxiliary continuous semantics. To enable effective workspace-to-register interaction, we further design a lightweight Hash Refinement Adapter that progressively refines the Hash Register across transformer layers. As a result, binary-oriented representations are formed through token evolution within the backbone, rather than being abruptly induced by an output-level projection. HashViT is optimized with a unified objective that combines learnable semantic center supervision, class-token similarity distillation, and quantization regularization, encouraging the HASH token to encode semantically structured and compact binary representations. Extensive experiments on three widely used benchmarks demonstrate that HashViT achieves state-of-the-art or highly competitive retrieval performance while preserving the efficiency of compact Hamming codes. Code is available at https://github.com/Xinze919/HashViT.","short_abstract":"Efficient large-scale image retrieval requires compact representations that preserve semantic similarity under fast Hamming-space search. Deep hashing is appealing, but most existing CNN- and ViT-based methods still follow a post-quantization paradigm, where continuous visual features are first learned and binary codes...","url_abs":"https://arxiv.org/abs/2607.03328","url_pdf":"https://arxiv.org/pdf/2607.03328v1","authors":"[\"Xinze Liu\",\"Ding Wang\",\"Hengjie Zhu\",\"Dayan Wu\"]","published":"2026-07-03T13:46:18Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.IR\"]","methods":"[\"Vision Transformer\",\"Transformer\",\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":613929,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T01:22:02.77346169Z","DeletedAt":null,"paper_id":5935738,"paper_url":"https://arxiv.org/abs/2607.03328","paper_title":"Beyond Post-Quantization: Native Hash Learning with a Dedicated HASH Token","repo_url":"https://github.com/Xinze919/HashViT","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}