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2D Classification

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코드

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

https://github.com/dadung/dge-cda

공식 코드

Assessing Domain Gap for Continual Domain Adaptation in Object Detection

https://github.com/daeunfullgrace/dyra

공식 코드

DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors

https://github.com/dahuilangda/biocheminsight

공식 코드

BioChemInsight: An Open-Source Toolkit for Automated Identification and Recognition of Optical Chemical Structures and Activity Data in Scientific Publications

https://github.com/daidaijr/eamamba

공식 코드

EAMamba: Efficient All-Around Vision State Space Model for Image Restoration

https://github.com/daijifeng001/r-fcn

공식 코드

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/dair-iitd/degpr

공식 코드

DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting

https://github.com/daishiresearch/transnext

공식 코드

TransNeXt: Robust Foveal Visual Perception for Vision Transformers

https://github.com/daitranskku/aic2024-track4-team15

공식 코드

Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets

https://github.com/daixinghome/Distill_GID_detectron2

공식 코드

General Instance Distillation for Object Detection

https://github.com/dali92002/DE-GAN

공식 코드

DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

https://github.com/dallascard/neural_topic_models

공식 코드

Neural Models for Documents with Metadata

https://github.com/dallascard/scholar

공식 코드

Neural Models for Documents with Metadata

https://github.com/damaggu/tadp

공식 코드

Text-image Alignment for Diffusion-based Perception

https://github.com/damianliumin/non-adversarial_backdoor

공식 코드

Beating Backdoor Attack at Its Own Game

https://github.com/damo-cv/GiraffeDet

공식 코드

GiraffeDet: A Heavy-Neck Paradigm for Object Detection

https://github.com/damo-cv/elsa

공식 코드

ELSA: Enhanced Local Self-Attention for Vision Transformer

https://github.com/damo-vilab/composer

공식 코드

Composer: Creative and Controllable Image Synthesis with Composable Conditions

https://github.com/danfenghong/ieee_tip_uiu-net

공식 코드

UIU-Net: U-Net in U-Net for Infrared Small Object Detection

https://github.com/danhntd/FS-CDIS

공식 코드

The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation

https://github.com/daniel-xsy/bev-attack

공식 코드

On the Adversarial Robustness of Camera-based 3D Object Detection

https://github.com/daniel03c1/masked_wavelet_nerf

공식 코드

Masked Wavelet Representation for Compact Neural Radiance Fields

https://github.com/daniel3303/storyreasoning

공식 코드

StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation

https://github.com/danielgol/hme-vas

공식 코드

Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction

https://github.com/danifranco/em_domain_adaptation

공식 코드

Deep learning based domain adaptation for mitochondria segmentation on EM volumes

https://github.com/danischober/labliquidvision

공식 코드

Vision-based robot manipulation of transparent liquid containers in a laboratory setting

https://github.com/danjacobellis/LCCL

공식 코드

Learned Compression for Compressed Learning

https://github.com/danjacobellis/mpq

공식 코드

Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models

https://github.com/danjacobellis/walloc

공식 코드

Learned Compression for Compressed Learning

https://github.com/danjos95/dade

공식 코드

DaDe: Delay-adaptive Detector for Streaming Perception

https://github.com/danqi/rc-cnn-dailymail

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A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

https://github.com/danruta/neat

공식 코드

NeAT: Neural Artistic Tracing for Beautiful Style Transfer

https://github.com/danyang-liu/KRED

공식 코드

KRED: Knowledge-Aware Document Representation for News Recommendations

https://github.com/daojiaxu/semeval_11

공식 코드

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

https://github.com/daoyuan98/odm

공식 코드

Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection

https://github.com/dapangliu/lamer

공식 코드

Non-Parallel Text Style Transfer with Self-Parallel Supervision

https://github.com/darglein/ADOP

공식 코드

ADOP: Approximate Differentiable One-Pixel Point Rendering

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