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/ehoogeboom/hexaconv
공식 코드https://github.com/ehsanik/muscleTorch
공식 코드What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
https://github.com/eihw/asc_sharpness
공식 코드https://github.com/elkhouryk/rs-transclip
공식 코드Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification
https://github.com/ericyu97/uad-rs
공식 코드Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
https://github.com/ermongroup/teochat
공식 코드TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
https://github.com/floatingstarZ/GeRSP
공식 코드Generic Knowledge Boosted Pre-training For Remote Sensing Images
https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning
공식 코드Geographical Knowledge-driven Representation Learning for Remote Sensing Images
https://github.com/fmenat/sits_s2coverage
공식 코드Assessment of Sentinel-2 spatial and temporal coverage based on the scene classification layer
https://github.com/gaopiaoliang/Evidential
공식 코드https://github.com/gasserelbanna/serab-byols
공식 코드BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
https://github.com/gomezzz/MSMatch
공식 코드MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
https://github.com/gomezzz/distmsmatch
공식 코드Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
https://github.com/google-research/google-research/tree/master/muNet
공식 코드A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
https://github.com/google-research/google-research/tree/master/remote_sensing_representations
공식 코드https://github.com/hackerekcah/distinct-events-asc
공식 코드Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
https://github.com/hvak/mimrf-bfm
공식 코드Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty
https://github.com/ibm/neural-embedding-compression
공식 코드Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling
https://github.com/idt-iti/visual-audio-discrepancy-detection
공식 코드https://github.com/intellisensing/rs-agent
공식 코드RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
https://github.com/jakobabesser/icassp_2022_disentanglementasc
공식 코드Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning
https://github.com/jessytan/low-complexity-asc
공식 코드Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
https://github.com/jiangtaoxie/MPN-COV
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/jiangtaoxie/fast-MPN-COV
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/jishengbai/audiolog
공식 코드AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
https://github.com/jishengbai/icme2024asc
공식 코드https://github.com/jmj2316/KAN_CNN/blob/main/kcn.py
공식 코드Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing
https://github.com/jordipons/neural-classifiers-with-few-audio
공식 코드https://github.com/keillernogueira/exploit-cnn-rs
공식 코드Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
https://github.com/kkoutini/cpjku_dcase19
공식 코드Receptive-field-regularized CNN variants for acoustic scene classification
https://github.com/kkoutini/cpjku_dcase20
공식 코드https://github.com/lemmersj/ground-truth-or-daer
공식 코드Ground-truth or DAER: Selective Re-query of Secondary Information
https://github.com/louisccc/av_av
공식 코드Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/lwcver/lwganet
공식 코드LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
https://github.com/mainaksingha01/applenet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.