When training or evaluating deep learning models, two essential parts are picking the
proper loss function and deciding on performance metrics. In this paper, we provide a …

Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …

Abstract Large-scale Vision-and-Language (V+ L) pre-training for representation learning
has proven to be effective in boosting various downstream V+ L tasks. However, when it …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Deep hashing has been intensively studied and successfully applied in large-scale image
retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that …

Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images
depicting the concept of interests (ie, the same sub-category labels) highest based on the …

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

In hash-based image retrieval systems, degraded or transformed inputs usually generate
different codes from the original, deteriorating the retrieval accuracy. To mitigate this issue …

In this paper, we propose S uppression-E nhancing M ask based attention and I nteractive C
hannel transformati ON (SEMICON) to learn binary hash codes for dealing with large-scale …