Papers with Code 아카이브

16k

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/euphoria16/ui-genie

공식 코드

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

https://github.com/event-ahu/openevdet

공식 코드

Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset

https://github.com/event-ahu/vfm-det

공식 코드

VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models

https://github.com/ewrfcas/MAE-FAR

공식 코드

Learning Prior Feature and Attention Enhanced Image Inpainting

https://github.com/exiawsh/streampetr

공식 코드

Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection

https://github.com/explainableml/bayescap

공식 코드

BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

https://github.com/explosion5/Dolphins

공식 코드

DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving

https://github.com/fabiankueppers/calibration-framework

공식 코드

Multivariate Confidence Calibration for Object Detection

https://github.com/fabienbaradel/object_level_visual_reasoning

공식 코드

Object Level Visual Reasoning in Videos

https://github.com/fabiencode/lg3d

공식 코드

Label-Guided Auxiliary Training Improves 3D Object Detector

https://github.com/fabiopoiesi/monet_dataset

공식 코드

The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios

https://github.com/facebookresearch/ConvNeXt

공식 코드

A ConvNet for the 2020s

https://github.com/facebookresearch/DepthContrast

공식 코드

Self-Supervised Pretraining of 3D Features on any Point-Cloud

https://github.com/facebookresearch/Detic

공식 코드

Detecting Twenty-thousand Classes using Image-level Supervision

https://github.com/facebookresearch/PLRC

공식 코드

Point-Level Region Contrast for Object Detection Pre-Training

https://github.com/facebookresearch/SlowFast

공식 코드

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/facebookresearch/adaptive_teacher

공식 코드

Cross-Domain Adaptive Teacher for Object Detection

https://github.com/facebookresearch/barlowtwins

공식 코드

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/facebookresearch/codellama

공식 코드

Code Llama: Open Foundation Models for Code

https://github.com/facebookresearch/convnext-v2

공식 코드

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

https://github.com/facebookresearch/cutler

공식 코드

Cut and Learn for Unsupervised Object Detection and Instance Segmentation

https://github.com/facebookresearch/deit

공식 코드

Augmenting Convolutional networks with attention-based aggregation

https://github.com/facebookresearch/detectron

공식 코드

Focal Loss for Dense Object Detection

https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2

공식 코드

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet

공식 코드

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/facebookresearch/detectron2/tree/master/projects/TensorMask

공식 코드

TensorMask: A Foundation for Dense Object Segmentation

https://github.com/facebookresearch/detr

공식 코드

End-to-End Object Detection with Transformers

https://github.com/facebookresearch/dino

공식 코드

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/facebookresearch/efm3d

공식 코드

EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models

https://github.com/facebookresearch/egoobjects

공식 코드

EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding

https://github.com/facebookresearch/fair_self_supervision_benchmark

공식 코드

Scaling and Benchmarking Self-Supervised Visual Representation Learning

https://github.com/facebookresearch/hiera

공식 코드

Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

https://github.com/facebookresearch/imvotenet

공식 코드

ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes

https://github.com/facebookresearch/long_seq_mae

공식 코드

Exploring Long-Sequence Masked Autoencoders

https://github.com/facebookresearch/mae

공식 코드

Masked Autoencoders Are Scalable Vision Learners

https://github.com/facebookresearch/maws

공식 코드

The effectiveness of MAE pre-pretraining for billion-scale pretraining

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