Papers with Code 아카이브
Spatial Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/fudan-zvg/spar
From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D
https://github.com/gengchenmai/se-kge
https://github.com/google-research/bigbird
https://github.com/gpt4life/alpagasus
https://github.com/guyd1995/lra-benchmark
https://github.com/guyd1995/lra-benchmarks
https://github.com/hj963434688/Faster_rcnn
https://github.com/huan123/py-fatser-rcnn
https://github.com/huggingface/transformers
https://github.com/ibrahimSouleiman/GuessWhat
GuessWhat?! Visual object discovery through multi-modal dialogue
https://github.com/insigh/Faster-RCNN-Tensorflow
https://github.com/isaaccorley/resize-is-all-you-need
https://github.com/ithuanhuan/gpu-py3-faster-rcnn
https://github.com/ithuanhuan/py-fatser-rcnn
https://github.com/kahnchana/locvlm
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
https://github.com/ken-power/SelfDrivingCarND-ExtendedKalmanFilter
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/ken-power/SensorFusionND-3D-Object-Tracking
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/lalithjets/global-reasoned-multi-task-model
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
https://github.com/lamalab-org/chem-bench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lamalab-org/chembench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lflage/openfactscore
https://github.com/lil-lab/ciff
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
https://github.com/linzhiqiu/clip-flant5
https://github.com/liuqiang3/faster_rcnn
https://github.com/llava-annonymous/llava
https://github.com/lllyasviel/omost
https://github.com/llmbev/talk2bev
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
https://github.com/mappy/tf-faster-rcnn
https://github.com/mengyingfei/faster-rcnn-tf
https://github.com/microsoft/eureka-ml-insights
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
https://github.com/nautilus261/tf-faster-rcnn
https://github.com/neuqgz/modify-faster-rcnn-tf
https://github.com/nickgkan/3d_diffuser_actor
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.