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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/xpq-tech/code-llmedit

공식 코드

Model Editing for LLMs4Code: How Far are We?

https://github.com/xternalz/AE-WTN

공식 코드

Scaling Object Detection by Transferring Classification Weights

https://github.com/xudangliatiger/ape-loss

공식 코드

Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection

https://github.com/xuebinqin/DIS

공식 코드

Highly Accurate Dichotomous Image Segmentation

https://github.com/xuefusiji/badrefsr

공식 코드

BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution

https://github.com/xueliancheng/hybridstereonet-main

공식 코드

Deep Laparoscopic Stereo Matching with Transformers

https://github.com/xuez-phd/efficient-rgb-t-early-fusion-detection

공식 코드

Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection

https://github.com/xuez-phd/tfdet

공식 코드

TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

https://github.com/xushilin1/dst-det

공식 코드

DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection

https://github.com/xuxw98/backtoreality

공식 코드

Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement

https://github.com/xuxw98/dspdet3d

공식 코드

3D Small Object Detection with Dynamic Spatial Pruning

https://github.com/xuxy09/RawSR

공식 코드

Towards Real Scene Super-Resolution with Raw Images

https://github.com/xuyangbai/transfusion

공식 코드

TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

https://github.com/xuyimin0926/sadc

공식 코드

Shadow-Aware Dynamic Convolution for Shadow Removal

https://github.com/xvjiarui/GCNet

공식 코드

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

https://github.com/xwmaxwma/tinyvim

공식 코드

TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba

https://github.com/xy-boy/ediffsr

공식 코드

EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

https://github.com/xy-guo/LIGA-Stereo

공식 코드

LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

https://github.com/xylgroup/mlmc

공식 코드

Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation

https://github.com/xyliu339/osdd

공식 코드

One-Step Diffusion Model for Image Motion-Deblurring

https://github.com/xzwlx/Difficulty-SR

공식 코드

Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network

https://github.com/y2863/MetaGraspNet

공식 코드

MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis

https://github.com/yahidar/radarformer

공식 코드

RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

https://github.com/yan-hao-tian/ConTNet

공식 코드

ConTNet: Why not use convolution and transformer at the same time?

https://github.com/yancie-yjr/StreamYOLO

공식 코드

Real-time Object Detection for Streaming Perception

https://github.com/yancie-yjr/dbq-ssd

공식 코드

DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection

https://github.com/yandex-research/distill-nf

공식 코드

Distilling the Knowledge from Conditional Normalizing Flows

https://github.com/yandex-research/gan_vs_diff_sr

공식 코드

Does Diffusion Beat GAN in Image Super Resolution?

https://github.com/yang-0201/MAF-YOLO

공식 코드

Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection

https://github.com/yang-0201/mhaf-yolo

공식 코드

MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection

https://github.com/yang-song/APDLM

공식 코드

Training Deep Neural Networks via Direct Loss Minimization

https://github.com/yang-yk/NP-RepMet

공식 코드

Restoring Negative Information in Few-Shot Object Detection

https://github.com/yangbincheng/emsrdpn

공식 코드

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning

https://github.com/yangcao1996/fanet

공식 코드

FakeMix Augmentation Improves Transparent Object Detection

https://github.com/yangcaoai/3dgs-det

공식 코드

3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection

https://github.com/yangcaoai/CoDA_NeurIPS2023

공식 코드

Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection

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