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2D Classification

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

https://github.com/rayguan97/M3DeTR

공식 코드

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

https://github.com/rayleizhu/biformer

공식 코드

BiFormer: Vision Transformer with Bi-Level Routing Attention

https://github.com/raysonlaroca/rodosol-alpr-dataset

공식 코드

On the Cross-dataset Generalization in License Plate Recognition

https://github.com/razaimam45/fine-tune-distillation

공식 코드

Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance

https://github.com/raziehkaviani/rdosr

공식 코드

Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery

https://github.com/razla/patch-of-invisibility

공식 코드

Patch of Invisibility: Naturalistic Physical Black-Box Adversarial Attacks on Object Detectors

https://github.com/rbgirshick/fast-rcnn

공식 코드

Fast R-CNN

https://github.com/rbgirshick/py-faster-rcnn

공식 코드

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/rbgirshick/voc-dpm

공식 코드

Scene-Specific Pedestrian Detection Based on Parallel Vision

https://github.com/rbturnbull/hespi

공식 코드

Hespi: A pipeline for automatically detecting information from hebarium specimen sheets

https://github.com/rd20karim/mb-ores

공식 코드

MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing

https://github.com/re-owod/re-owod

공식 코드

Revisiting Open World Object Detection

https://github.com/reactivetype/simrod

공식 코드

SimROD: A Simple Adaptation Method for Robust Object Detection

https://github.com/realcrane/student-splating-scooping

공식 코드

3D Student Splatting and Scooping

https://github.com/realtime-intelligence/evrt-detr

공식 코드

EvRT-DETR: Latent Space Adaptation of Image Detectors for Event-based Vision

https://github.com/redaigc/cq-dino

공식 코드

CQ-DINO: Mitigating Gradient Dilution via Category Queries for Vast Vocabulary Object Detection

https://github.com/reddragon/efficient-dl-survey-paper

공식 코드

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

https://github.com/redrock303/NeRFLiX_CPVR2023

공식 코드

NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer

https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration

공식 코드

Revisiting Temporal Alignment for Video Restoration

https://github.com/redwankarimsony/weightperturbation-msu

공식 코드

Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection

https://github.com/reglab/land-application-detection

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Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act

https://github.com/reiinakano/neural-painters-pytorch

공식 코드

Neural Painters: A learned differentiable constraint for generating brushstroke paintings

https://github.com/rem105-210/difffashion

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DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

https://github.com/remaro-network/kd-yolox-vit

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Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection

https://github.com/remaro-network/rosar-framework

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ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection

https://github.com/renanrojasg/adv_robust_autoencoder

공식 코드

Inverting Adversarially Robust Networks for Image Synthesis

https://github.com/renjithk4/assemai

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AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines

https://github.com/renll/RSAM

공식 코드

Recurrent Soft Attention Model for Common Object Recognition

https://github.com/renmengye/imageqa-public

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Exploring Models and Data for Image Question Answering

https://github.com/renyurui/pirender

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PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering

https://github.com/repertorium/hq-sod-generator

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SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation

https://github.com/repertorium/synthsod-baseline

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SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation

https://github.com/reproducibilityproject/effortly

공식 코드

Laying foundations to quantify the "Effort of Reproducibility"

https://github.com/researchmm/sttr

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Fine-Grained Image Style Transfer with Visual Transformers

https://github.com/retkowsky/florence-2

공식 코드

Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

https://github.com/retsuh-bqw/fmp

공식 코드

Adversarial Feature Map Pruning for Backdoor

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