https://github.com/buildingamind/vit-cot
공식 코드Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bxiang233/panopticsegforlargescalepointcloud
공식 코드Towards accurate instance segmentation in large-scale LiDAR point clouds
https://github.com/byangderek/CRAFT
공식 코드https://github.com/byeongkeun-kang/HOI-dataset
공식 코드Hand Segmentation for Hand-Object Interaction from Depth map
https://github.com/bytedance/dq-det
공식 코드Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
https://github.com/caicch/OS-WSGIC
공식 코드Top-Down Framework for Weakly-supervised Grounded Image Captioning
https://github.com/caiqi/cascasde-3d
공식 코드3D Cascade RCNN: High Quality Object Detection in Point Clouds
https://github.com/caiqi/sample-weighting-network
공식 코드Learning a Unified Sample Weighting Network for Object Detection
https://github.com/caiyingfeng/vrso
공식 코드VRSO: Visual-Centric Reconstruction for Static Object Annotation
https://github.com/calc-project/object-naming-data
공식 코드Everybody Likes to Sleep: A Computer-Assisted Comparison of Object Naming Data from 30 Languages
https://github.com/callsys/genpromp
공식 코드Generative Prompt Model for Weakly Supervised Object Localization
https://github.com/camma-public/surglatentgraph
공식 코드Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer
https://github.com/cancam/LRP
공식 코드Localization Recall Precision (LRP): A New Performance Metric for Object Detection
https://github.com/canerozer/explainable-iqa
공식 코드https://github.com/canoe-z/petdet
공식 코드PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
https://github.com/canonical-capsules/canonical-capsules
공식 코드Canonical Capsules: Self-Supervised Capsules in Canonical Pose
https://github.com/carlobretti/object-scene-compositions-for-actions
공식 코드Zero-Shot Action Recognition from Diverse Object-Scene Compositions
https://github.com/carrell-ncl/windscreen2
공식 코드Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking
https://github.com/carrierlxk/GraphMemVOS
공식 코드Video Object Segmentation with Episodic Graph Memory Networks
https://github.com/carrierlxk/MuG
공식 코드https://github.com/casia-iva-lab/obj2seq
공식 코드Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
https://github.com/casia-iva-lab/zbs
공식 코드https://github.com/cccorn/AP-loss
공식 코드https://github.com/cdevin/objectattention
공식 코드Deep Object-Centric Representations for Generalizable Robot Learning
https://github.com/cea-list/mt-detr
공식 코드Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
https://github.com/cfederer/TrainCNNsWithNeuralData
공식 코드https://github.com/cfzd/monoground
공식 코드https://github.com/cgraber/psf-diffattn
공식 코드Joint Forecasting of Panoptic Segmentations with Difference Attention
https://github.com/ch3cook-fdu/vote2cap-detr
공식 코드https://github.com/changhaonan/ovsg
공식 코드Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
https://github.com/changhaonan/star-no-prior
공식 코드Scene-level Tracking and Reconstruction without Object Priors
https://github.com/chanind/linear-relational-concepts
공식 코드Identifying Linear Relational Concepts in Large Language Models
https://github.com/chanyn/HKRM
공식 코드Hybrid Knowledge Routed Modules for Large-scale Object Detection
https://github.com/chaoqichen/HTCN
공식 코드Harmonizing Transferability and Discriminability for Adapting Object Detectors
https://github.com/charles-xie/cql
공식 코드Category Query Learning for Human-Object Interaction Classification
https://github.com/charlesq34/frustum-pointnets
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.