https://github.com/ducha-aiki/affnet
공식 코드Repeatability Is Not Enough: Learning Affine Regions via Discriminability
Papers with Code 아카이브
Image Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Image Retrieval is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside classification and cross-modal retrieval. By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.
Extending CLIP for Category-to-image Retrieval in E-commerce
<span style="color:grey; opacity: 0.6">( Image credit: DELF )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ducminhkhoi/Amodal-Instance-Seg-ASBU
공식 코드A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
https://github.com/dvl-tum/group_loss
공식 코드https://github.com/dvl-tum/intra_batch
공식 코드https://github.com/eccv22-ood-workshop/eccv22-ood-workshop.github.io
공식 코드Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
https://github.com/ecom-research/ComposeAE
공식 코드Compositional Learning of Image-Text Query for Image Retrieval
https://github.com/eduard6421/iqpp
공식 코드https://github.com/eduard6421/pqpp
공식 코드PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
https://github.com/egeozsoy/LF-SGG
공식 코드https://github.com/elias-ramzi/happier
공식 코드Hierarchical Average Precision Training for Pertinent Image Retrieval
https://github.com/elias-ramzi/roadmap
공식 코드Robust and Decomposable Average Precision for Image Retrieval
https://github.com/elias-ramzi/suprank
공식 코드https://github.com/emerisly/edis
공식 코드EDIS: Entity-Driven Image Search over Multimodal Web Content
https://github.com/emiliojorge/Inventing-a-Grounded-Language
공식 코드Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
https://github.com/endomapper/EM_Dataset-GeometricCalibration
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/EM_Dataset-ToolSegmentation
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/em_dataset-orbslam3validation
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/em_dataset-photometriccalibration
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/ercbunny/isaacgymenvs
공식 코드Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing
https://github.com/ethz-asl/lcd
공식 코드LCD -- Line Clustering and Description for Place Recognition
https://github.com/eunyi-lyou/MA-ZS-SBIR
공식 코드Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval
https://github.com/explainableml/sketch-primitives
공식 코드https://github.com/explainableml/vision_by_language
공식 코드Vision-by-Language for Training-Free Compositional Image Retrieval
https://github.com/exponentiai/arnet
공식 코드ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling
https://github.com/exponentiai/effnet
공식 코드ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling
https://github.com/facebookresearch/dino
공식 코드https://github.com/facebookresearch/dinov2
공식 코드https://github.com/facebookresearch/multigrain
공식 코드MultiGrain: a unified image embedding for classes and instances
https://github.com/facebookresearch/vilbert-multi-task
공식 코드12-in-1: Multi-Task Vision and Language Representation Learning
https://github.com/fandulu/IHDA
공식 코드Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
https://github.com/fartashf/vsepp
공식 코드VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/fcakir/mihash
공식 코드https://github.com/ferchonavarro/shape_aware_segmentation
공식 코드Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
https://github.com/feymanpriv/DOLG
공식 코드DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
https://github.com/filipradenovic/cnnimageretrieval-pytorch
공식 코드https://github.com/fiveai/hardest
공식 코드Query-based Hard-Image Retrieval for Object Detection at Test Time
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.