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2D Classification

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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/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

https://github.com/facebookresearch/mils

공식 코드

LLMs can see and hear without any training

https://github.com/facebookresearch/mmf/tree/main/projects/unit

공식 코드

UniT: Multimodal Multitask Learning with a Unified Transformer

https://github.com/facebookresearch/mvit

공식 코드

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/facebookresearch/nerf-det

공식 코드

NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection

https://github.com/facebookresearch/omni3d

공식 코드

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

https://github.com/facebookresearch/projectaria_tools

공식 코드

Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception

https://github.com/facebookresearch/r-mae

공식 코드

R-MAE: Regions Meet Masked Autoencoders

https://github.com/facebookresearch/self_defeating_improvements

공식 코드

Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems

https://github.com/facebookresearch/side

공식 코드

Improving Wikipedia Verifiability with AI

https://github.com/facebookresearch/speech-resynthesis

공식 코드

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations

https://github.com/facebookresearch/sylph-few-shot-detection

공식 코드

Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

https://github.com/facebookresearch/unbiased-teacher

공식 코드

Unbiased Teacher for Semi-Supervised Object Detection

https://github.com/facebookresearch/unbiased-teacher-v2

공식 코드

Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

https://github.com/facebookresearch/video-nonlocal-net

공식 코드

Non-local Neural Networks

https://github.com/facebookresearch/vlpart

공식 코드

Going Denser with Open-Vocabulary Part Segmentation

https://github.com/facebookresearch/votenet

공식 코드

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/facebookresearch/xcit

공식 코드

XCiT: Cross-Covariance Image Transformers

https://github.com/facias914/A-Simple-Detector-is-a-Strong-Tracker

공식 코드

A Simple Detector with Frame Dynamics is a Strong Tracker

https://github.com/facias914/sood-mcl

공식 코드

Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection

https://github.com/factslab/famus

공식 코드

FAMuS: Frames Across Multiple Sources

https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging

공식 코드

Transformers in Medical Imaging: A Survey

https://github.com/faisaltareque/XL-HeadTags

공식 코드

XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags

https://github.com/fajri91/sum_liputan6

공식 코드

Liputan6: A Large-scale Indonesian Dataset for Text Summarization

https://github.com/fallenshock/SinDDM

공식 코드

SinDDM: A Single Image Denoising Diffusion Model

https://github.com/fangleai/Implicit-LVM

공식 코드

Implicit Deep Latent Variable Models for Text Generation

https://github.com/fangyanwen1106/RLANet

공식 코드

Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation

https://github.com/fangyi-chen/sqr

공식 코드

Enhanced Training of Query-Based Object Detection via Selective Query Recollection

https://github.com/fanq15/FewX

공식 코드

Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

https://github.com/fanq15/GCoNet

공식 코드

Group Collaborative Learning for Co-Salient Object Detection

https://github.com/fanyang587/MGL

공식 코드

Mutual Graph Learning for Camouflaged Object Detection

https://github.com/fanyihua0309/friendnet

공식 코드

FriendNet: Detection-Friendly Dehazing Network

https://github.com/faridghm/meta-sel

공식 코드

3D-model ShapeNet Core Classification using Meta-Semantic Learning

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