Papers with Code 아카이브

Object Detection

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전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/tiankongzhang/mga

공식 코드

Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment

https://github.com/tiankongzhang/mgada

공식 코드

Multi-Granularity Alignment Domain Adaptation for Object Detection

https://github.com/tianweiy/CenterPoint

공식 코드

Center-based 3D Object Detection and Tracking

https://github.com/tianweiy/MVP

공식 코드

Multimodal Virtual Point 3D Detection

https://github.com/tianzhi0549/FCOS

공식 코드

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/tiffamy/3dod-lsf

공식 코드

Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features

https://github.com/timbervision/timbervision

공식 코드

TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations

https://github.com/timbroed/MUSES

공식 코드

MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty

https://github.com/timbroed/hrfuser

공식 코드

HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection

https://github.com/timschoonbeek/industreal

공식 코드

IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting

https://github.com/timy90022/DropLoss

공식 코드

DropLoss for Long-Tail Instance Segmentation

https://github.com/timy90022/One-Shot-Object-Detection

공식 코드

One-Shot Object Detection with Co-Attention and Co-Excitation

https://github.com/tingxueronghua/dpke

공식 코드

Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection

https://github.com/tingz123/fha-kitchens

공식 코드

Multi-Granularity Hand Action Detection

https://github.com/tinh2044/YOLO12-UnderWater

공식 코드

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation

https://github.com/tiny-smart/dysample

공식 코드

Learning to Upsample by Learning to Sample

https://github.com/tiny-smart/sapa

공식 코드

On Point Affiliation in Feature Upsampling

https://github.com/tinytigerpan/bckd

공식 코드

Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection

https://github.com/tinyvision/damo-yolo

공식 코드

DAMO-YOLO : A Report on Real-Time Object Detection Design

https://github.com/tjiagom/stochastic-yolo

공식 코드

Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts

https://github.com/tjiiv-cprg/MonoRUn

공식 코드

MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

https://github.com/tjiiv-cprg/epro-pnp

공식 코드

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

https://github.com/tjiiv-cprg/epro-pnp-v2

공식 코드

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

https://github.com/tjradarlab/tj4dradset

공식 코드

TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

https://github.com/tjubiit/TJU-DHD

공식 코드

TJU-DHD: A Diverse High-Resolution Dataset for Object Detection

https://github.com/tkyen1110/tednet

공식 코드

Tracking-Assisted Object Detection with Event Cameras

https://github.com/tldoan/-hyp-ow-aaai-2024-

공식 코드

Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection

https://github.com/tmiu/itfa

공식 코드

Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

https://github.com/tmlabonte/weakly-supervised-DETR

공식 코드

Scaling Novel Object Detection with Weakly Supervised Detection Transformers

https://github.com/tobiasriedlinger/cmppp-object-detection

공식 코드

Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection

https://github.com/tobiasriedlinger/gradient-metrics-od

공식 코드

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

https://github.com/toelt-llc/FlightScope_Bench

공식 코드

FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery

https://github.com/toelt-llc/cloud_segmentation_comparative

공식 코드

BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery

https://github.com/tolgabkm/hrplanes

공식 코드

A benchmark dataset for deep learning-based airplane detection: HRPlanes

https://github.com/tom-roddick/mono-semantic-maps

공식 코드

Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks

https://github.com/tonyfang04/8-calves

공식 코드

8-Calves Image dataset

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