Papers with Code 아카이브
2D Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dyxm/pm-hmcw
공식 코드https://github.com/dyxm/vcvw-3d
공식 코드VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations
https://github.com/dyzhang09/sam3d
공식 코드SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model
https://github.com/dzcgaara/HuBo-VLM
공식 코드HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasks
https://github.com/dzungdoan6/tc-pdm
공식 코드TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation
https://github.com/dzungdoan6/wstta
공식 코드Weakly Supervised Test-Time Domain Adaptation for Object Detection
https://github.com/e-bug/weak-relation-vlm
공식 코드Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining
https://github.com/eBay/modanet
공식 코드ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
https://github.com/eaphan/nclr
공식 코드Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
https://github.com/eaphan/stemd
공식 코드Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection
https://github.com/eaphan/upidet
공식 코드Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
https://github.com/easonxiao-888/grootvl
공식 코드https://github.com/easywalk-prin/opennav
공식 코드OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation
https://github.com/ebennequin/FewShotVision
공식 코드https://github.com/ecker-lab/object-centric-representation-benchmark
공식 코드Benchmarking Unsupervised Object Representations for Video Sequences
https://github.com/edithlzh/VNL_Estimation
공식 코드https://github.com/edm-research/cad2render
공식 코드https://github.com/edm-research/dimo_objectdetection
공식 코드Analysis of Training Object Detection Models with Synthetic Data
https://github.com/edm-research/genetic-augment
공식 코드Genetic Learning for Designing Sim-to-Real Data Augmentations
https://github.com/edornd/contrastive-distillation
공식 코드A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
https://github.com/eduardo-candioto-fidelis/raw-radar-data-generation
공식 코드https://github.com/eduardohenriquearnold/coop-3dod-infra
공식 코드Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors
https://github.com/edward-sun/tsp-detection
공식 코드Rethinking Transformer-based Set Prediction for Object Detection
https://github.com/edwarddo69/d3t
공식 코드https://github.com/eeic-lab/dreb-net
공식 코드https://github.com/efs-opensource/calibration-framework
공식 코드Bayesian Confidence Calibration for Epistemic Uncertainty Modelling
https://github.com/eg4000/SKU110K_CVPR19
공식 코드https://github.com/egulsoylu/image-ais-fusion
공식 코드Image and AIS Data Fusion Technique for Maritime Computer Vision Applications
https://github.com/eighonet/parking-research
공식 코드Revising deep learning methods in parking lot occupancy detection
https://github.com/eitanspi/weather-image-classification
공식 코드https://github.com/eithun/cherry-phyllotaxy
공식 코드https://github.com/ekut-es/mr3d-net
공식 코드MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception
https://github.com/elischwartz/maeday
공식 코드https://github.com/elvintanhust/monocd
공식 코드MonoCD: Monocular 3D Object Detection with Complementary Depths
https://github.com/emanuelevivoli/CoMix
공식 코드CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.