Papers with Code 아카이브
Content-Based Image Retrieval
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Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.
Source: Camera Obscurer: Generative Art for Design Inspiration
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HighwayWu/SIFT-Reconstruction
공식 코드Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction
https://github.com/IVIPLab/FBSNet
공식 코드FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
https://github.com/MultimodalGeo/GeoText-1652
공식 코드Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
https://github.com/PyRetri/PyRetri
공식 코드https://github.com/Skyy93/Sample4Geo
공식 코드Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
https://github.com/TIBHannover/iart
공식 코드iART: A Search Engine for Art-Historical Images to Support Research in the Humanities
https://github.com/Visual-Computing/GPR1200
공식 코드GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval
https://github.com/Xuanmeng-Zhang/gnn-re-ranking
공식 코드Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
https://github.com/Yux1angJi/GTA-UAV
공식 코드https://github.com/arnejad/smikm
공식 코드Saliency Map-based Image Retrieval using Invariant Krawtchouk Moments
https://github.com/azgo14/classification_metric_learning
공식 코드Classification is a Strong Baseline for Deep Metric Learning
https://github.com/barleah/greedyal
공식 코드Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
https://github.com/canhld94/daejeon520
공식 코드https://github.com/cszhanglmu/obd-finder
공식 코드OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery
https://github.com/cvjena/ITAL
공식 코드Information-Theoretic Active Learning for Content-Based Image Retrieval
https://github.com/cvjena/aid
공식 코드Automatic Query Image Disambiguation for Content-Based Image Retrieval
https://github.com/cvjena/eu-flood-dataset
공식 코드Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
https://github.com/dmmm1997/fsra
공식 코드A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
https://github.com/eduard6421/iqpp
공식 코드https://github.com/ercbunny/isaacgymenvs
공식 코드Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing
https://github.com/gaoshuang98/cvcities
공식 코드CV-Cities: Advancing Cross-View Geo-Localization in Global Cities
https://github.com/jakhac/csmae
공식 코드Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
https://github.com/kirk86/ImageRetrieval
공식 코드https://github.com/kwadraterry/norppa
공식 코드NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation
https://github.com/layumi/Image-Text-Embedding
공식 코드Dual-Path Convolutional Image-Text Embeddings with Instance Loss
https://github.com/layumi/University1652-Baseline
공식 코드University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
https://github.com/levelfour/pumil
공식 코드Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
https://github.com/li-xirong/jingwei
공식 코드Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
https://github.com/liuzrcc/PIRE
공식 코드https://github.com/lt-asset/selp
공식 코드SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
https://github.com/mcc-wh/csa
공식 코드Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
https://github.com/mic-dkfz/foundation-models-for-cbmir
공식 코드Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology
https://github.com/microsoft/AirSim-Drone-Racing-VAE-Imitation
공식 코드Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations
https://github.com/mida-group/crossmodal_imgretrieval
공식 코드Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations
https://github.com/mzhang367/hpdh
공식 코드Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.