Papers with Code 아카이브

Object Detection

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하위 태스크

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평가표

124

보존된 평가 기록

977

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/torchdsp/torchsig

공식 코드

Large Scale Radio Frequency Wideband Signal Detection & Recognition

https://github.com/torrvision/straighttoshapes

공식 코드

Straight to Shapes: Real-time Detection of Encoded Shapes

https://github.com/tortuetortue/irpeoplecounting

공식 코드

Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting

https://github.com/tosshero/3d_perception

공식 코드

Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

https://github.com/tosshero/ros_packages

공식 코드

Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

https://github.com/towardsautonomy/VR3Dense

공식 코드

VR3Dense: Voxel Representation Learning for 3D Object Detection and Monocular Dense Depth Reconstruction

https://github.com/towardsautonomy/datasetequity

공식 코드

DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets

https://github.com/tpoellabauer/ycb-luma

공식 코드

YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization

https://github.com/tpopordanoska/calibration-object-detection

공식 코드

Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection

https://github.com/trailab/dino_teacher

공식 코드

Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection

https://github.com/trailab/gmir

공식 코드

Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection

https://github.com/trailab/hypermodest

공식 코드

HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering

https://github.com/trailab/pdv

공식 코드

Point Density-Aware Voxels for LiDAR 3D Object Detection

https://github.com/trailab/uncertaintytrack

공식 코드

UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking

https://github.com/tranleanh/adas_od_enhance

공식 코드

Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)

https://github.com/tranleanh/edn-gtm

공식 코드

A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing

https://github.com/trbg/vagus-nerve-u-net

공식 코드

A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection

https://github.com/tri-ml/dd3d

공식 코드

Is Pseudo-Lidar needed for Monocular 3D Object detection?

https://github.com/tri-ml/vedet

공식 코드

Viewpoint Equivariance for Multi-View 3D Object Detection

https://github.com/trumpetertimes/sparse-generation

공식 코드

Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume

https://github.com/tryhiseyyysum/menol

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Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving

https://github.com/tshizys/CoupleNet

공식 코드

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

https://github.com/tsinghua-mars-lab/geomae

공식 코드

GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training

https://github.com/tsingqguo/ABBA

공식 코드

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

https://github.com/tsingqguo/jadena

공식 코드

Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection

https://github.com/ttaoREtw/ImGeoNet

공식 코드

ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

https://github.com/tteepe/EarlyBird

공식 코드

EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View

https://github.com/tteepe/tracktacular

공식 코드

Lifting Multi-View Detection and Tracking to the Bird's Eye View

https://github.com/tudelft-iv/unibev

공식 코드

UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities

https://github.com/tue-mps/refnet

공식 코드

A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data

https://github.com/tum-traffic-dataset/coopdet3d

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TUMTraf V2X Cooperative Perception Dataset

https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit

공식 코드

TUMTraf V2X Cooperative Perception Dataset

https://github.com/tumftm/caliv

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CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups via Object Reconstruction

https://github.com/tumftm/dpft

공식 코드

DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection

https://github.com/tumftm/radargnn

공식 코드

RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

https://github.com/tumftm/sim2realdistributionaligneddataset

공식 코드

Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.