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평가표

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보존된 평가 기록

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코드

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

https://github.com/mzeegers/DeepFODDataGenerator

공식 코드

A tomographic workflow to enable deep learning for X-ray based foreign object detection

https://github.com/n-friederich/airport_fence_inspection

공식 코드

Security Fence Inspection at Airports Using Object Detection

https://github.com/n-kubiak/s3r-net

공식 코드

S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal

https://github.com/n-nejatishahidin/pose_from_mid-level

공식 코드

Object Pose Estimation using Mid-level Visual Representations

https://github.com/naagar/art_restoration_dm

공식 코드

Adaptation of the super resolution SOTA for Art Restoration in camera capture images

https://github.com/naamiinepal/smartphone_microscopy

공식 코드

Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images

https://github.com/nab170130/new_streamline

공식 코드

STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings

https://github.com/nalemadi/VME_CDSI_dataset_benchmark

공식 코드

VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond

https://github.com/namhoonlee/snip-public

공식 코드

SNIP: Single-shot Network Pruning based on Connection Sensitivity

https://github.com/naomiex/mdha

공식 코드

MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection

https://github.com/naoto0804/SynShadow

공식 코드

Learning from Synthetic Shadows for Shadow Detection and Removal

https://github.com/naoto0804/cross-domain-detection

공식 코드

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

https://github.com/nasir6/zero_shot_detection

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Synthesizing the Unseen for Zero-shot Object Detection

https://github.com/naston/stableremaster

공식 코드

Stable Remaster: Bridging the Gap Between Old Content and New Displays

https://github.com/natezhenghy/udke

공식 코드

Unfolded Deep Kernel Estimation for Blind Image Super-resolution

https://github.com/nathan66666/dvmsr

공식 코드

DVMSR: Distillated Vision Mamba for Efficient Super-Resolution

https://github.com/nathan66666/hasn

공식 코드

HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution

https://github.com/naver-ai/egtr

공식 코드

EGTR: Extracting Graph from Transformer for Scene Graph Generation

https://github.com/naver-ai/pit

공식 코드

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/naver-ai/rdnet

공식 코드

DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs

https://github.com/naver-ai/relabel_imagenet

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Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

https://github.com/naver-ai/vidt

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ViDT: An Efficient and Effective Fully Transformer-based Object Detector

https://github.com/naver/croco

공식 코드

CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

https://github.com/naver/shine

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

https://github.com/navervision/mlsd

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Towards Light-weight and Real-time Line Segment Detection

https://github.com/navervision/tsst

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Task Specific Attention is one more thing you need for object detection

https://github.com/nazmul-karim170/FIP-Fisher-Backdoor-Removal

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Fisher Information guided Purification against Backdoor Attacks

https://github.com/nazmul-karim170/NFT-Augmented-Backdoor-Purification

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Augmented Neural Fine-Tuning for Efficient Backdoor Purification

https://github.com/nblt/twa

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Trainable Weight Averaging: A General Approach for Subspace Training

https://github.com/ncarraz/ESRGANplus

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ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network

https://github.com/ncherel/psal

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Patch-Based Stochastic Attention for Image Editing

https://github.com/ndrplz/semiparametric

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Warp and Learn: Novel Views Generation for Vehicles and Other Objects

https://github.com/ndwxhmzz/un-detr

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UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection

https://github.com/neeharperi/futuredet

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Forecasting from LiDAR via Future Object Detection

https://github.com/nenadmarkus/pico

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Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

https://github.com/neouyghur/sess

공식 코드

SESS: Saliency Enhancing with Scaling and Sliding

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