Search Results for author:
Found 8 papers, 1 papers with code
Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks
According to the findings, we further propose a novel importance sampling-based, element-wise joint optimization method, called Adaptively Mask Subnetworks Considering Modal Significance(AMSS).
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
Multiple Code Hashing for Efficient Image Retrieval
In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search.
SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval
With the explosive growth of video data in real applications, near-duplicate video retrieval (NDVR) has become indispensable and challenging, especially for short videos.
Deep Multi-Index Hashing for Person Re-Identification
In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID.
On the Evaluation Metric for Hashing
Bucket search, also called hash lookup, can achieve fast query speed with a sub-linear time cost based on the inverted index table constructed from hash codes.
Asymmetric Deep Supervised Hashing
However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points.
