https://github.com/CVMI-Lab/SlotCon
공식 코드Self-Supervised Visual Representation Learning with Semantic Grouping
Papers with Code 아카이브
Object Discovery
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Object Discovery is the task of identifying previously unseen objects.
Source: Unsupervised Object Discovery and Segmentation of RGBD-images
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Cloud-CV/object-proposals
공식 코드https://github.com/EIDOSLAB/pruning-for-vision-representation
공식 코드https://github.com/Genera1Z/GroupedDiscreteRepresentation
공식 코드https://github.com/Genera1Z/VQ-VFM-OCL
공식 코드Vector-Quantized Vision Foundation Models for Object-Centric Learning
https://github.com/Jiawei-Yang/Denoising-ViT
공식 코드https://github.com/MarcBS/Ego-Object_Discovery
공식 코드https://github.com/MasterBin-IIAU/UNINEXT
공식 코드Universal Instance Perception as Object Discovery and Retrieval
https://github.com/TonyLianLong/RCF-UnsupVideoSeg
공식 코드Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
https://github.com/adambielski/move-seg
공식 코드MOVE: Unsupervised Movable Object Segmentation and Detection
https://github.com/agopal42/ctcae
공식 코드Contrastive Training of Complex-Valued Autoencoders for Object Discovery
https://github.com/agopal42/syncx
공식 코드Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
https://github.com/ais-bonn/unsupervised-decomposition-pcdnet
공식 코드Unsupervised Image Decomposition with Phase-Correlation Networks
https://github.com/aitor-martinez-seras/snn-automotive-object-detection
공식 코드https://github.com/alexanderrubinstein/occam
공식 코드https://github.com/angelvillar96/Unsupervised-Decomposition-PCDNet-1
공식 코드Unsupervised Image Decomposition with Phase-Correlation Networks
https://github.com/applied-ai-lab/genesis
공식 코드GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
https://github.com/arahosu/musicslots
공식 코드https://github.com/autonomousvision/akorn
공식 코드https://github.com/baldassarrefe/iclr-osc-22
공식 코드Towards Self-Supervised Learning of Global and Object-Centric Representations
https://github.com/billpsomas/simpool
공식 코드Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
https://github.com/boschresearch/what-matters-for-meta-learning
공식 코드https://github.com/chaoningzhang/mobilesam
공식 코드https://github.com/cvg/frontiernet
공식 코드https://github.com/cvmi-lab/codet
공식 코드CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
https://github.com/deepmind/spriteworld
공식 코드https://github.com/dirtyharrylyl/hake-ava
공식 코드Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions
https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
https://github.com/eyalgomel/box-based-refinement
공식 코드Box-based Refinement for Weakly Supervised and Unsupervised Localization Tasks
https://github.com/facebookresearch/dinov2
공식 코드https://github.com/feipanir/moda
공식 코드https://github.com/gicheonkang/prograsp
공식 코드PROGrasp: Pragmatic Human-Robot Communication for Object Grasping
https://github.com/google-research/google-research/tree/master/invariant_slot_attention
공식 코드Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
https://github.com/google-research/google-research/tree/master/slot_attention
공식 코드https://github.com/hasibzunair/peekaboo
공식 코드PEEKABOO: Hiding parts of an image for unsupervised object localization
https://github.com/hatins/deoe
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.