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/igor-morawski/raw-nod

공식 코드

GenISP: Neural ISP for Low-Light Machine Cognition

https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds

공식 코드

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

https://github.com/iit-pavis/look_around_and_learn

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Look Around and Learn: Self-Training Object Detection by Exploration

https://github.com/ika-rwth-aachen/multicorrupt

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MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

https://github.com/ikatsamenis/cone-detection

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TraCon: A novel dataset for real-time traffic cones detection using deep learning

https://github.com/iliiliiliili/tanet-near-far

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Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems

https://github.com/imankgoyal/NonDeepNetworks

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Non-deep Networks

https://github.com/imatge-upc/detection-2016-nipsws

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Hierarchical Object Detection with Deep Reinforcement Learning

https://github.com/imatge-upc/retrieval-2016-deepvision

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Faster R-CNN Features for Instance Search

https://github.com/imatif17/Prototype-Mean-Teacher

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Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher

https://github.com/imatif17/acia

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Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

https://github.com/imfinethanks/esbnn

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Expanding-and-Shrinking Binary Neural Networks

https://github.com/imnotprepared/airshot

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AirShot: Efficient Few-Shot Detection for Autonomous Exploration

https://github.com/imoonlab/hyper-yolo

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Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

https://github.com/imoonlab/yolov13

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YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

https://github.com/implabunipr/unbiased-teacher

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Improving Localization for Semi-Supervised Object Detection

https://github.com/implus/GFocal

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Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

https://github.com/implus/GFocalV2

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Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

https://github.com/implus/PytorchInsight

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Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

https://github.com/implus/RecursiveMix-pytorch

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RecursiveMix: Mixed Learning with History

https://github.com/implus/um-mae

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Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality

https://github.com/imrl/umad

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UMAD: University of Macau Anomaly Detection Benchmark Dataset

https://github.com/imruljubair/BdSLImset

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Real Time Bangladeshi Sign Language Detection using Faster R-CNN

https://github.com/inakierregueab/yolo11-jde

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YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID

https://github.com/inconstance/t-shirt-natural-backdoor-dataset

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TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World

https://github.com/industryessentials/ymir

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ALBench: A Framework for Evaluating Active Learning in Object Detection

https://github.com/inexascale/pychop

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Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks

https://github.com/infinity7428/hANMCL

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Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning

https://github.com/insdet/instance-detection

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A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture

https://github.com/inspire-group/ObjectSeeker

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ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking

https://github.com/inspire-group/detectorguard

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DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks

https://github.com/intcomp/camouflaged-vlm

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Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models

https://github.com/inverted-ai/object-detection

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Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models

https://github.com/iqtlabs/camolo

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The Weaknesses of Adversarial Camouflage in Overhead Imagery

https://github.com/iranqin/supfusion

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SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection

https://github.com/ireneperezsalesa/event_tracking

공식 코드

Event-based Visual Tracking in Dynamic Environments

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