Papers with Code 아카이브
Image Retrieval
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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/vignywang/awesome-local-feature-matching
공식 코드https://github.com/visual-computing/las_flas
공식 코드https://github.com/visual-haystacks/vhs_benchmark
공식 코드Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
https://github.com/vl2g/cofar
공식 코드https://github.com/vuvko/fitw2020
공식 코드Achieving Better Kinship Recognition Through Better Baseline
https://github.com/walbouss/gem
공식 코드Grounding Everything: Emerging Localization Properties in Vision-Language Transformers
https://github.com/waltersimoncini/fungivision
공식 코드No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
https://github.com/wang-ml-lab/multimodal-needle-in-a-haystack
공식 코드https://github.com/wangck20/idml
공식 코드https://github.com/wangguanan/denoiserep
공식 코드https://github.com/wangxu-scu/CoDA
공식 코드Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
https://github.com/wdr123/darp-sbir
공식 코드Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval
https://github.com/weitong8591/vop
공식 코드Breaking the Frame: Visual Place Recognition by Overlap Prediction
https://github.com/weiweisong415/Demo_AHCL_for_TGRS2022
공식 코드Asymmetric Hash Code Learning for Remote Sensing Image Retrieval
https://github.com/wesleyzhang1991/google_landmark_retrieval_2021_2nd_place_solution
공식 코드https://github.com/whats2000/WeiMoCIR
공식 코드Training-free Zero-shot Composed Image Retrieval via Weighted Modality Fusion and Similarity
https://github.com/wickstrom/clinical-self-supervised-cbir-ct-liver
공식 코드A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
https://github.com/willemsenbram/a-game-of-sorts
공식 코드https://github.com/willemsenbram/reference-resolution-via-text-generation
공식 코드Resolving References in Visually-Grounded Dialogue via Text Generation
https://github.com/willemsenbram/reg-with-guiding
공식 코드https://github.com/wjpoom/spec
공식 코드Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
https://github.com/workingcoder/MCJA
공식 코드https://github.com/wtyhub/LPN
공식 코드Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
https://github.com/wtyhub/MuseNet
공식 코드Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
https://github.com/wzzheng/HDML
공식 코드https://github.com/wzzheng/idml
공식 코드https://github.com/xandery-geek/SAAT
공식 코드Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
https://github.com/xialeiliu/CrowdCountingCVPR18
공식 코드Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
https://github.com/xiangyu-mm/unifashion
공식 코드UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation
https://github.com/xiaowu0162/visualize-then-retrieve
공식 코드https://github.com/xiaoyuan1996/AMFMN
공식 코드Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
https://github.com/xiaoyuan1996/galr
공식 코드Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
https://github.com/xsc1234/invisible-relevance-bias
공식 코드Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
https://github.com/xunguangwang/ProS-GAN
공식 코드Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
https://github.com/xunguangwang/cgat
공식 코드CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.