Papers with Code 아카이브
3D Character Animation From A Single Photo
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Image: Weng et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/turingmotors/nuscenes-mqa
공식 코드https://github.com/usc-sail/mica-MovieCLIP
공식 코드https://github.com/vitae-transformer/rsp
공식 코드https://github.com/vitae-transformer/vitae-transformer-remote-sensing
공식 코드https://github.com/vpulab/Semantic-Aware-Scene-Recognition
공식 코드https://github.com/wang-chen/interestingness
공식 코드Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
https://github.com/wanglimin/MRCNN-Scene-Recognition
공식 코드Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
https://github.com/wanglimin/Places205-VGGNet
공식 코드https://github.com/wangzheallen/vsad
공식 코드Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
https://github.com/wbhu/BPNet
공식 코드Bidirectional Projection Network for Cross Dimension Scene Understanding
https://github.com/xupine/DFPENet
공식 코드https://github.com/yeyimilk/deep-learning-for-manatee-counting
공식 코드Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel
https://github.com/yjxiong/caffe
공식 코드Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
https://github.com/zn-qiao/deep-narrow-network
공식 코드https://bitbucket.org/tsungyu/bcnn
https://github.com/Aniket7/Transfer-Learning
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/Bazingaliu/learning_CSRNet
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/BiQiWHU/Attention-based-Multi-instance-CNN
https://github.com/BiQiWHU/LSENet
https://github.com/CommissarMa/CSRNet-pytorch
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/DeepSceneSeg/SSMA
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
https://github.com/DiaoXY/CSRnet
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/Harrypotterrrr/DVD-GAN
https://github.com/JepsonWong/CNN_Visualization
https://github.com/Kadenze/siamese_net
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vgg
https://github.com/Neerajj9/CSRNet-keras
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/RTalha/CROWD-COUNTING-USING-CSRNET
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/Saritus/Crowd-Counter
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/UCB-ICSI-Vision-Group/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/UCBAIR/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/ahmedest61/vlad-buff
Deep Attentional Structured Representation Learning for Visual Recognition
https://github.com/anwesanpal/DEDUCE
DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments
https://github.com/baldassarreFe/deep-koalarization
CNN Features off-the-shelf: an Astounding Baseline for Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.