https://github.com/abin333/logonet
공식 코드LogoNet: a fine-grained network for instance-level logo sketch retrieval
Papers with Code 아카이브
Image Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 56
- 보존된 평가 기록
- 372
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/adlnlp/attention_vl
공식 코드https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10
공식 코드https://github.com/adobe-research/polar-vl
공식 코드Improving Personalized Search with Regularized Low-Rank Parameter Updates
https://github.com/ahmedest61/vlad-buff
공식 코드VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition
https://github.com/aimagelab/safe-clip
공식 코드Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
https://github.com/ais-lab/descriptorsynthesis4feat2map
공식 코드Leveraging Neural Radiance Field in Descriptor Synthesis for Keypoints Scene Coordinate Regression
https://github.com/ajd12342/why-winoground-hard
공식 코드Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality
https://github.com/akshitac8/biam
공식 코드https://github.com/alex-oesterling/multigroup-proportional-representation
공식 코드https://github.com/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/alibaba/AliceMind
공식 코드mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/alijavidani/local_global_representation_learning
공식 코드Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained Approach
https://github.com/alirezazareian/vspnet
공식 코드https://github.com/alishiraligit/eval-on-laion
공식 코드https://github.com/amaralibey/gpm
공식 코드https://github.com/amaralibey/gsv-cities
공식 코드GSV-Cities: Toward Appropriate Supervised Visual Place Recognition
https://github.com/amaralibey/mixvpr
공식 코드https://github.com/anasibrahim/ctl_classification
공식 코드Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
https://github.com/anastasiakrith/multimodal-retrieval-for-vwsd
공식 코드Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
https://github.com/andreineculai/mpc
공식 코드Probabilistic Compositional Embeddings for Multimodal Image Retrieval
https://github.com/angelvillar96/STLPose
공식 코드https://github.com/anguoyuan/pixel_retrieval-segmented_instance_retrieval
공식 코드Towards Content-based Pixel Retrieval in Revisited Oxford and Paris
https://github.com/anon96652/oneshotclip
공식 코드https://github.com/apchenstu/SLN-Amodal
공식 코드https://github.com/apple/ml-mofi
공식 코드MOFI: Learning Image Representations from Noisy Entity Annotated Images
https://github.com/apple/ml-vfm-kt
공식 코드https://github.com/applied-ai-research-lab/orchestrator-agent-trust
공식 코드https://github.com/arnejad/smikm
공식 코드Saliency Map-based Image Retrieval using Invariant Krawtchouk Moments
https://github.com/ashespt/advbct
공식 코드Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval
https://github.com/avinashsai/mml
공식 코드https://github.com/aviveise/2WayNet
공식 코드https://github.com/ayankumarbhunia/adaptive-fgsbir
공식 코드https://github.com/ayankumarbhunia/stroke_subset_selector-for-fgsbir
공식 코드Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.