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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/lisavilalee/scaresnet_mmdet

공식 코드

SCAResNet: A ResNet Variant Optimized for Tiny Object Detection in Transmission and Distribution Towers

https://github.com/lisaweijler/fate

공식 코드

FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data

https://github.com/lishaoxu1994/diffstyler

공식 코드

DiffStyler: Diffusion-based Localized Image Style Transfer

https://github.com/lishaoxu1994/instant-nerf-stylization

공식 코드

Instant Photorealistic Neural Radiance Fields Stylization

https://github.com/lisiyaoATbnu/crcnet

공식 코드

Iterative Residual Image Deconvolution

https://github.com/lisiyaoATbnu/low_rank_kernel

공식 코드

Understanding Kernel Size in Blind Deconvolution

https://github.com/litterbrother-xiao/overview-of-non-autoregressive-applications

공식 코드

A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

https://github.com/littlepey/ptpm

공식 코드

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

https://github.com/liturout/psld

공식 코드

Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models

https://github.com/liu-sd/multi-output-deblur

공식 코드

Multi-Outputs Is All You Need For Deblur

https://github.com/liuchen1997/rfaconv

공식 코드

RFAConv: Innovating Spatial Attention and Standard Convolutional Operation

https://github.com/liudongyang6/fcfd

공식 코드

Function-Consistent Feature Distillation

https://github.com/liuliu-55/rofusion

공식 코드

ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion

https://github.com/liusongxiang/StarGAN-Voice-Conversion

공식 코드

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

https://github.com/liutongkun/comad

공식 코드

Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

https://github.com/liuwei16/CSP

공식 코드

Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection

https://github.com/liuweijie19980216/DRL-for-FSOD

공식 코드

Dynamic Relevance Learning for Few-Shot Object Detection

https://github.com/liuxingbin/dbot

공식 코드

Exploring Target Representations for Masked Autoencoders

https://github.com/liuxinhai/FG3D-Net

공식 코드

Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions

https://github.com/liuyang-ict/awesome-visual-transformers

공식 코드

A Survey of Visual Transformers

https://github.com/liuyang-ict/sap-detr

공식 코드

SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency

https://github.com/liuyi1989/pwrf

공식 코드

Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding

https://github.com/liuyi1989/seamless-detection

공식 코드

Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection

https://github.com/liuyuan-pal/NeuRay

공식 코드

Neural Rays for Occlusion-aware Image-based Rendering

https://github.com/liuyuan-pal/nero

공식 코드

NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images

https://github.com/liuyugeng/baadd

공식 코드

Backdoor Attacks Against Dataset Distillation

https://github.com/liuziyu77/rar

공식 코드

RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition

https://github.com/liuziyu77/visual-rft

공식 코드

Visual-RFT: Visual Reinforcement Fine-Tuning

https://github.com/liuzywen/cod

공식 코드

Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer

https://github.com/liuzywen/hrtransnet

공식 코드

HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

https://github.com/liuzywen/lfscribble

공식 코드

LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection

https://github.com/liuzywen/rgbtscribble-icme2023

공식 코드

Scribble-Supervised RGB-T Salient Object Detection

https://github.com/liuzywen/ssfam

공식 코드

SSFam: Scribble Supervised Salient Object Detection Family

https://github.com/liuzywen/swinnet

공식 코드

SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection

https://github.com/liuzywen/tritransnet

공식 코드

TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network

https://github.com/livebench/livebench

공식 코드

LiveBench: A Challenging, Contamination-Limited LLM Benchmark

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