https://github.com/yonghaoxu/uae-rs
공식 코드Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Papers with Code 아카이브
Scene Classification
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Scene Classification is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.
Source: Scene classification with Convolutional Neural Networks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuanbo2020/ergl
공식 코드https://github.com/yuwchen/CITISEN
공식 코드CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
https://github.com/zhangtong1/transfer_learning_cspt
공식 코드https://github.com/zhepeiw/clap_curation
공식 코드Unsupervised Improvement of Audio-Text Cross-Modal Representations
https://github.com/zhu-xlab/decur
공식 코드Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
https://github.com/zhu-xlab/unida
공식 코드Universal Domain Adaptation for Remote Sensing Image Scene Classification
https://github.com/zwx8981/liqe
공식 코드Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
https://github.com/AICPS/roadscene2vec
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/ArealTeamM2AIC/Remote-Sensing-Image
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/ArturoDeza/NeuroFovea_PyTorch
https://github.com/BiQiWHU/Attention-based-Multi-instance-CNN
https://github.com/BiQiWHU/LSENet
https://github.com/JepsonWong/CNN_Visualization
https://github.com/KrishnaDN/acoustic-scene-analysis-with-multihead-self-attention
https://github.com/MLEnthusiast/MHCLN
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
https://github.com/MaDongao/SiftingGAN
https://github.com/MorningStarOvO/dissect
Understanding the Role of Individual Units in a Deep Neural Network
https://github.com/OptimusPrimus/dcase2020_workshop
https://github.com/XavierJiezou/cloud-removal-deploy
https://github.com/andychinka/dcase-challenge
A multi-device dataset for urban acoustic scene classification
https://github.com/arampacha/clip-rsicd
Exploring Models and Data for Remote Sensing Image Caption Generation
https://github.com/asprout/CPSC490
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
https://github.com/canturan10/satellighte
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/chen-yang-liu/change-agent
RSMamba: Remote Sensing Image Classification with State Space Model
https://github.com/chen-yang-liu/rscama
RSMamba: Remote Sensing Image Classification with State Space Model
https://github.com/craston/object_detection_cib
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
https://github.com/cuzyoung/crossearth
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
https://github.com/danfenghong/IEEE_TPAMI_SpectralGPT
https://github.com/danielrossi1/takunet
https://github.com/edufonseca/icassp19
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
https://github.com/hvak/bi-michi
Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty
https://github.com/lavender105/rsgpt
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/louisccc/sg-risk-assessment
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/lucashu1/land-cover
https://github.com/lx709/vrsbench
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.