Papers with Code 아카이브

2D Object Detection

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하위 태스크

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평가표

20

보존된 평가 기록

57

코드

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

https://github.com/beiyan1911/players-iod

공식 코드

Refined Response Distillation for Class-Incremental Player Detection

https://github.com/believeinlain/asynch-cv

공식 코드

Real-Time Event-Based Tracking and Detection for Maritime Environments

https://github.com/bellatong/meunet

공식 코드

Multi-scale Edge-based U-shape Network for Salient Object Detection

https://github.com/bellybeauty/mdsam

공식 코드

Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection

https://github.com/ben57882/c2fnet-tscvt

공식 코드

Camouflaged Object Detection via Context-aware Cross-level Fusion

https://github.com/benbergner/cropr

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Token Cropr: Faster ViTs for Quite a Few Tasks

https://github.com/benjaminrwilson/range-view-3d-detection

공식 코드

What Matters in Range View 3D Object Detection

https://github.com/berkeley-hipie/hipie

공식 코드

Hierarchical Open-vocabulary Universal Image Segmentation

https://github.com/bertinetto/siamese-fc

공식 코드

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/bethgelab/imagecorruptions

공식 코드

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/mmdetection

공식 코드

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/robust-detection-benchmark

공식 코드

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/siamese-mask-rcnn

공식 코드

One-Shot Instance Segmentation

https://github.com/bethgelab/stylize-datasets

공식 코드

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/better-chao/perceptual_abilities_evaluation

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Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation

https://github.com/bgu-cs-vil/wtconv

공식 코드

Wavelet Convolutions for Large Receptive Fields

https://github.com/bic-l/burst-ann2snn

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Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks

https://github.com/biclab/spike-driven-transformer-v3

공식 코드

Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training

https://github.com/biclab/spikeyolo

공식 코드

Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection

https://github.com/big-feather/PICR-Net_ACMMM23_MS

공식 코드

Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection

https://github.com/bigvideoresearch/OIM

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Object Instance Mining for Weakly Supervised Object Detection

https://github.com/bigwangyudong/lqit

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Is Underwater Image Enhancement All Object Detectors Need?

https://github.com/bilel-bj/tau

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TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems

https://github.com/bimsarapathiraja/mccl

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Multiclass Confidence and Localization Calibration for Object Detection

https://github.com/bingqingzhang/odd-vod

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Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection

https://github.com/bingykang/Fewshot_Detection

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Few-shot Object Detection via Feature Reweighting

https://github.com/binh24399/d-close

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Towards Better Explanations for Object Detection

https://github.com/binyisu/food

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Towards Generalized Few-Shot Open-Set Object Detection

https://github.com/binyisu/fsosod

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Towards Generalized Few-Shot Open-Set Object Detection

https://github.com/bipin-saha/bnvd

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Bangladeshi Native Vehicle Detection in Wild

https://github.com/bit-da/o2net

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Improving Transferability for Domain Adaptive Detection Transformers

https://github.com/bjork-crypto/visled-querying

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Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

https://github.com/bjzhb666/GS-LoRA

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Continual Forgetting for Pre-trained Vision Models

https://github.com/bladewaltz1/clipvid

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Identity-Consistent Aggregation for Video Object Detection

https://github.com/blakechen97/sasa

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SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection

https://github.com/blessing988/pavecap

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PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation

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