Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ldery/ATTITTUD
공식 코드Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral
https://github.com/leahcimali/Comparative-Evaluation-of-Clustered-Federated-Learning-Methods
공식 코드Comparative Evaluation of Clustered Federated Learning Methods
https://github.com/leaplabthu/agent-attention
공식 코드Agent Attention: On the Integration of Softmax and Linear Attention
https://github.com/leaplabthu/dat
공식 코드https://github.com/leaplabthu/dynamic_perceiver
공식 코드https://github.com/leaplabthu/lasnet
공식 코드https://github.com/leaplabthu/proco
공식 코드Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
https://github.com/leapmind/ern
공식 코드https://github.com/leduckhai/litegpt
공식 코드LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task
https://github.com/leeguandong/DACNet-for-HSI
공식 코드Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification
https://github.com/leeguandong/DGCNet-for-HSI
공식 코드https://github.com/leeguandong/EKGNet-for-HSI
공식 코드Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification
https://github.com/leeguandong/KANet-for-HSI
공식 코드Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification
https://github.com/leeguandong/MVNet-for-HSI
공식 코드https://github.com/leeguandong/SGDSCNet-for-HSI
공식 코드https://github.com/leeguandong/STNet-for-HSI
공식 코드https://github.com/leeguandong/WCNet-for-HSI
공식 코드3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification
https://github.com/leeguandong/fsknet-for-hsi
공식 코드https://github.com/leekeyu/abdominal_ultrasound_classification
공식 코드https://github.com/leekyungmoon/XDED
공식 코드Cross-Domain Ensemble Distillation for Domain Generalization
https://github.com/leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification
공식 코드Task Discrepancy Maximization for Fine-grained Few-Shot Classification
https://github.com/leeyeehoo/GenNAS
공식 코드https://github.com/leezekun/vitst
공식 코드Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
https://github.com/leftthomas/PSCapsNet
공식 코드https://github.com/lehaifeng/RSI-CB
공식 코드RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
https://github.com/lehduong/kesi
공식 코드https://github.com/leirebv/synthetic_histopathology_dataset
공식 코드Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
https://github.com/leiyi-hu/mona
공식 코드https://github.com/lelo204/classificationmetricsforimageexplanations
공식 코드Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations
https://github.com/leonardopetrini/diffeo-sota
공식 코드Relative stability toward diffeomorphisms indicates performance in deep nets
https://github.com/leondada/fedapt
공식 코드Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
https://github.com/leonlha/e2e-3m
공식 코드Rethinking Recurrent Neural Networks and Other Improvements for Image Classification
https://github.com/leonlha/pso-convnet-dynamics
공식 코드PSO-Convolutional Neural Networks with Heterogeneous Learning Rate
https://github.com/lepangdan/Structure-Preserving-Transformation
공식 코드Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples
https://github.com/leymir/hyperbolic-image-embeddings
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.