Papers Deep Hashing
“Deep Hashing” 태그가 달린 논문 149편 · 필터 해제
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing technique…
ChunkingDeep HashingPrompt EngineeringRAG+3Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing
Large-scale image retrieval using deep hashing has become increasingly popular due to the exponential growth of image data and the remarkable feature extraction capabilities of deep neural networks (DNNs). However, deep …
Data PoisoningDeep HashingImage RetrievalRetrievalSelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
Recent studies show that the visual place recognition (VPR) method using pre-trained visual foundation models can achieve promising performance. In our previous work, we propose a novel method to realize seamless adaptat…
Deep HashingGPURe-RankingRetrieval+1ScNeuGM: Scalable Neural Graph Modeling for Coloring-Based Contention and Interference Management in Wi-Fi 7
Carrier-sense multiple access with collision avoidance in Wi-Fi often leads to contention and interference, thereby increasing packet losses. These challenges have traditionally been modeled as a graph, with stations (ST…
Collision AvoidanceDeep HashingManagementKALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving…
Deep Hashingparameter-efficient fine-tuningA Flexible Plug-and-Play Module for Generating Variable-Length
Deep supervised hashing has become a pivotal technique in large-scale image retrieval, offering significant benefits in terms of storage and search efficiency. However, existing deep supervised hashing models predominant…
Deep HashingImage RetrievalRetrievalDeep Class-guided Hashing for Multi-label Cross-modal Retrieval
Deep hashing, due to its low cost and efficient retrieval advantages, is widely valued in cross-modal retrieval. However, existing cross-modal hashing methods either explore the relationships between data points, which i…
Cross-Modal RetrievalDeep HashingRetrievalHybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for Image Retrieval
Deep image hashing aims to map input images into simple binary hash codes via deep neural networks and thus enable effective large-scale image retrieval. Recently, hybrid networks that combine convolution and Transformer…
Deep HashingImage RetrievalRetrievalLeveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature …
Deep HashingImage RetrievalRetrievalAttribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. …
AttributeDeep HashingImage ReconstructionImage Retrieval+1Deep Hashing via Householder Quantization
Hashing is at the heart of large-scale image similarity search, and recent methods have been substantially improved through deep learning techniques. Such algorithms typically learn continuous embeddings of the data. To …
BinarizationDeep HashingImage Similarity SearchMetric Learning+1Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
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 the existence of adversarial examples…
Adversarial AttackAdversarial RobustnessDeep HashingImage Retrieval+1Deep supervised hashing for fast retrieval of radio image cubes
The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing algorithms have been shown to be effici…
AstronomyDeep HashingImage RetrievalRetrievalSupervised Auto-Encoding Twin-Bottleneck Hashing
Deep hashing has shown to be a complexity-efficient solution for the Approximate Nearest Neighbor search problem in high dimensional space. Many methods usually build the loss function from pairwise or triplet data point…
Deep HashingTripletTowards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase due to the adoption of more sophisticated models and large-scale datasets. Dataset Distillation (DD) or Dataset …
Dataset CondensationDataset DistillationDeep HashingDiversity+1ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch
We present ElasticHash, a novel approach for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in …
Deep HashingImage Similarity SearchRetrievalSemantic Image SimilarityUnsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval
The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits …
Deep HashingDeep LearningDenoisingImage Retrieval+2Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval
Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness. Recently, several adversarial attacks have been presented to reveal the vulnerability of deep hashing models a…
Adversarial RobustnessDeep HashingImage RetrievalMath+1Unsupervised Hashing with Similarity Distribution Calibration
Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often overlook the fact that the similarity betwee…
Deep HashingImage RetrievalRetrievalDeep Hashing With Minimal-Distance-Separated Hash Centers
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 similarities in randomly sampled mini-batches. …
Deep HashingImage RetrievalQuantizationRetrieval