https://github.com/343gltysprk/ovow
공식 코드From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
Papers with Code 아카이브
Open World Object Detection
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Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ArminLee/OWOD_Review
공식 코드https://github.com/JosephKJ/OWOD
공식 코드https://github.com/VDIGPKU/OpenAD
공식 코드OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
https://github.com/ais-clemson/visiongpt
공식 코드VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation
https://github.com/akshitac8/ow-detr
공식 코드https://github.com/autonomousvision/good
공식 코드GOOD: Exploring Geometric Cues for Detecting Objects in an Open World
https://github.com/boschresearch/hyp-ow
공식 코드https://github.com/frh23333/mepu-owod
공식 코드Unsupervised Recognition of Unknown Objects for Open-World Object Detection
https://github.com/hatins/deoe
공식 코드https://github.com/idea-research/dino-x-api
공식 코드DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
https://github.com/johnwuzh/uc-owod
공식 코드https://github.com/lmb-freiburg/locov
공식 코드Localized Vision-Language Matching for Open-vocabulary Object Detection
https://github.com/lxycopper/plu
공식 코드Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
https://github.com/mcahny/object_localization_network
공식 코드Learning Open-World Object Proposals without Learning to Classify
https://github.com/mmaaz60/mvits_for_class_agnostic_od
공식 코드Class-agnostic Object Detection with Multi-modal Transformer
https://github.com/orrzohar/prob
공식 코드PROB: Probabilistic Objectness for Open World Object Detection
https://github.com/pku-icst-mipl/sia-ovd_acmmm2024
공식 코드SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary Detection
https://github.com/re-owod/re-owod
공식 코드https://github.com/sahalshajim/ss-owformer
공식 코드https://github.com/scuwyh2000/randbox
공식 코드https://github.com/surrey-uplab/recognize-any-regions
공식 코드https://github.com/thu-mig/yolo-uniow
공식 코드https://github.com/tldoan/-hyp-ow-aaai-2024-
공식 코드https://github.com/went-liang/unsniffer
공식 코드Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects
https://github.com/xiaomabufei/dowb
공식 코드https://github.com/xiaomabufei/skdf
공식 코드https://github.com/zhenyuw16/unidetector
공식 코드Detecting Everything in the Open World: Towards Universal Object Detection
https://github.com/Gaurav14cs17/YOLOE
Learning Open-World Object Proposals without Learning to Classify
https://github.com/KostadinovShalon/oln-mask
Learning Open-World Object Proposals without Learning to Classify
https://github.com/chwilms/sos
Learning Open-World Object Proposals without Learning to Classify
https://github.com/ibaiGorordo/ONNX-Object-Localization-Network
Learning Open-World Object Proposals without Learning to Classify
https://github.com/josephkj/eli
https://github.com/kostadinovshalon/oln-ssos
Learning Open-World Object Proposals without Learning to Classify
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.