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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/frankkramer-lab/deepgleason
공식 코드DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
https://github.com/frankkramer-lab/ensmic
공식 코드https://github.com/fraunhofer-iais/dlplatform/tree/master/DLplatform
공식 코드Efficient Decentralized Deep Learning by Dynamic Model Averaging
https://github.com/freedomintelligence/trim
공식 코드Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs
https://github.com/freeformrobotics/divide-and-co-training
공식 코드Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training
https://github.com/frejberglind/xood
공식 코드XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
https://github.com/fsherry/non-expansive-odes
공식 코드Designing Stable Neural Networks using Convex Analysis and ODEs
https://github.com/fshp971/encoder-inference
공식 코드Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services
https://github.com/fsschneider/deepobs
공식 코드https://github.com/fudan-zvg/SETR
공식 코드Vision Transformers: From Semantic Segmentation to Dense Prediction
https://github.com/fudan-zvg/dgmn2
공식 코드https://github.com/fudan-zvg/seaformer
공식 코드SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
https://github.com/fudanvi/maecho
공식 코드https://github.com/fukexue/fast
공식 코드FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
https://github.com/fukuchan41/tayloroe
공식 코드https://github.com/function2-llx/pumit
공식 코드Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks
https://github.com/fundamentalvision/Uni-Perceiver
공식 코드Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
https://github.com/fushenghao/privacypreservingml
공식 코드https://github.com/fuxiailab/protosemi
공식 코드Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
https://github.com/fuying-wang/mgca
공식 코드Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
https://github.com/fyu/drn
공식 코드https://github.com/g-benton/learning-invariances
공식 코드https://github.com/gabriellm2003/remote_sensing_missing_data
공식 코드Facing the Void: Overcoming Missing Data in Multi-View Imagery
https://github.com/gaia-vision/gaia-ssl
공식 코드https://github.com/ganatra-v/gaussian-sampling-fsl
공식 코드Logarithm-transform aided Gaussian Sampling for Few-Shot Learning
https://github.com/ganatra-v/precise
공식 코드https://github.com/ganglii/devsafety
공식 코드A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
https://github.com/ganlumomo/thermal-uda-attention
공식 코드Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
https://github.com/gao-xiyuan/fct
공식 코드Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images
https://github.com/gaobb/MCAR
공식 코드Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
https://github.com/gaoliang13/feddc
공식 코드FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
https://github.com/gaoliyao/Replica_Exchange_Stochastic_Gradient_MCMC
공식 코드Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
https://github.com/gaopengcuhk/Container
공식 코드https://github.com/gaopengcuhk/ballad
공식 코드A Simple Long-Tailed Recognition Baseline via Vision-Language Model
https://github.com/gaozhitong/multishiftseg
공식 코드Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.