https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mingxingtan/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/minhanh151/pre
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhanh151/respro
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhncedutw/pointnet1_keras
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/minostauros/VIAR
Unsupervised Learning of View-invariant Action Representations
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mirthai/csa-net
https://github.com/mitya8128/experiments_notes
https://github.com/ml-jku/cloob
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlbio-epfl/turtle
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlfoundations/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlfoundations/open_flamingo
https://github.com/mlzxzhou/keras-gnm
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/moabitcoin/ig65m-pytorch
Large-scale weakly-supervised pre-training for video action recognition
https://github.com/moein-shariatnia/OpenAI-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mohammadimathstar/grlgq
https://github.com/moojink/openvla-oft
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
https://github.com/morganmcg1/stanford-cars
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mpuu00001/Siformer
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
https://github.com/mryoo/pooled_time_series
https://github.com/mtanti/coco-caption
https://github.com/mtlouie-unm/alome-tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/musicalOffering/sola
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
https://github.com/musikisomorphie/swd
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
https://github.com/mustafa1728/TA3N-Lightning
https://github.com/muzairkhattak/multimodal-prompt-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mvenouziou/Project-Attention-Is-What-You-Get
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mx-mark/dmjd
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/mx-mark/videotransformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/myaldiz/deep_violence_detection
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/myc159/Deep-Taylor-Decomposition
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/mzolfaghari/chained-multistream-networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.