Title: Image Hashing via Cross-View Code Alignment in the Age of Foundation Models
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Image Hashing, Image Retrieval, Cross-View Alignment, Coding-Rate Maximization, Foundation Models

Abstract: Efficient large-scale retrieval requires representations that are both compact and discriminative. Foundation models provide powerful visual and multimodal embeddings, but nearest neighbor search in these high-dimensional spaces is computationally expensive. Hashing offers an efficient alternative by enabling fast Hamming distance search with binary codes, yet existing approaches often rely on complex pipelines, multi-term objectives, designs specialized for a single learning paradigm, and long training times. We introduce CroVCA (Cross-View Code Alignment), a simple and unified principle for learning binary codes that remain consistent across semantically aligned views. A single binary cross-entropy loss enforces alignment, while coding-rate maximization serves as an anti-collapse regularizer to promote balanced and diverse codes. To implement this, we design HashCoder, a lightweight MLP hashing network with a final batch normalization layer to enforce balanced codes. HashCoder can be used as a probing head on frozen embeddings or to adapt encoders efficiently via LoRA fine-tuning. Across benchmarks, CroVCA achieves state-of-the-art results in just 5 training epochs. At 16 bits, it performs particularly well—for instance, unsupervised hashing on COCO completes in under 2 minutes and supervised hashing on ImageNet100 in about 3 minutes—on a single GPU. These results highlight CroVCA's efficiency, adaptability, and broad applicability.

TL;DR: We propose cross-view code alignment, a simple and universal principle for hashing foundation model embeddings using binary cross-entropy and coding-rate maximization, unifying unsupervised and supervised hashing.