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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/hustvl/azinorm

공식 코드

AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception

https://github.com/hustvl/featurized-queryrcnn

공식 코드

Featurized Query R-CNN

https://github.com/hustvl/mim4d

공식 코드

MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning

https://github.com/hustvl/mimdet

공식 코드

Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection

https://github.com/hustvl/sparseinst

공식 코드

Sparse Instance Activation for Real-Time Instance Segmentation

https://github.com/hustvl/vim

공식 코드

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/hustvl/weaksam

공식 코드

WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition

https://github.com/hustvl/yolop

공식 코드

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/huuxiaobin/hitnet

공식 코드

High-resolution Iterative Feedback Network for Camouflaged Object Detection

https://github.com/huyvvo/bib

공식 코드

Active Learning Strategies for Weakly-supervised Object Detection

https://github.com/hvision-nku/camoformer

공식 코드

CamoFormer: Masked Separable Attention for Camouflaged Object Detection

https://github.com/hvision-nku/controlsr

공식 코드

ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution

https://github.com/hvision-nku/conv2former

공식 코드

Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

https://github.com/hvision-nku/srformer

공식 코드

SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution

https://github.com/hvxlab/hvdetfusion

공식 코드

HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

https://github.com/hwang1996/Meta-Concepts-for-Video-Captioning

공식 코드

Cross-Modal Graph with Meta Concepts for Video Captioning

https://github.com/hy0523/mtnet

공식 코드

Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation

https://github.com/hyenal/relate

공식 코드

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

https://github.com/hyeon-jo/interchange-transfer-kd

공식 코드

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

https://github.com/hyjhkoh/mgtanet

공식 코드

MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection

https://github.com/hyt35/prox-qn

공식 코드

Provably Convergent Plug-and-Play Quasi-Newton Methods

https://github.com/hyunjs/sgt

공식 코드

Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

https://github.com/hyz-xmaster/VarifocalNet

공식 코드

VarifocalNet: An IoU-aware Dense Object Detector

https://github.com/hyz-xmaster/swa_object_detection

공식 코드

SWA Object Detection

https://github.com/hzai-zjnu/mamba-yolo

공식 코드

Mamba YOLO: A Simple Baseline for Object Detection with State Space Model

https://github.com/hzai-zjnu/remdet

공식 코드

RemDet: Rethinking Efficient Model Design for UAV Object Detection

https://github.com/hzhupku/DCNet

공식 코드

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

https://github.com/hzx-9894/mma

공식 코드

Smart Feature is What You Need

https://github.com/hzxie/CityDreamer

공식 코드

CityDreamer: Compositional Generative Model of Unbounded 3D Cities

https://github.com/hzxie/CityDreamer4D

공식 코드

CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities

https://github.com/hzxie/GaussianCity

공식 코드

GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation

https://github.com/hzxie/awesome-3d-scene-generation

공식 코드

3D Scene Generation: A Survey

https://github.com/i-gao/adavision

공식 코드

Adaptive Testing of Computer Vision Models

https://github.com/i2-multimedia-lab/cdformer

공식 코드

CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution

https://github.com/i2-multimedia-lab/dsat

공식 코드

Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution

https://github.com/iVMCL/AOGNet-v2

공식 코드

Attentive Normalization

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