https://github.com/han-yuan-med/foundation-model
공식 코드Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning
Papers with Code 아카이브
Image Classification
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- 평가표
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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/hangg7/deformable-kernels/
공식 코드Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
https://github.com/hanjianghu/camera-motion-smoothing
공식 코드Robustness Certification of Visual Perception Models via Camera Motion Smoothing
https://github.com/hanktseng131415go/UPANets
공식 코드UPANets: Learning from the Universal Pixel Attention Networks
https://github.com/hanktseng131415go/pugd
공식 코드Perturbated Gradients Updating within Unit Space for Deep Learning
https://github.com/hanlu-nju/revisiting-uml
공식 코드Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
https://github.com/hanminghao/mscpt
공식 코드MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
https://github.com/hanzhu97702/dsnet
공식 코드Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification
https://github.com/haohang96/mra
공식 코드https://github.com/haohanwang/robustar
공식 코드Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
https://github.com/haojunyu1998/large_voc_seg
공식 코드https://github.com/haojunyu1998/larvseg
공식 코드https://github.com/haoliangwang86/la-ood
공식 코드Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
https://github.com/haolsun/WarPI
공식 코드https://github.com/haoqiwang/vim
공식 코드https://github.com/haotian-liu/LLaVA
공식 코드https://github.com/haoyangzheng1996/r2sgld
공식 코드Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
https://github.com/haoyu-r/how-to-manage-tinyml-at-scale
공식 코드On-device Online Learning and Semantic Management of TinyML Systems
https://github.com/haozheliu-st/dynamicfeatureaggregation
공식 코드https://github.com/harboryuan/ovsam
공식 코드Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
https://github.com/haria/SqueezeNet
공식 코드SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/harryanthony/mahalanobis-ood-detection
공식 코드https://github.com/harsh9524/coca
공식 코드Capsule Network based Contrastive Learning of Unsupervised Visual Representations
https://github.com/harshays/inputgradients
공식 코드https://github.com/harveyhuang18/emr_merging
공식 코드https://github.com/harveyp123/iccd_sptrn_slr
공식 코드https://github.com/hashimsayed0/transformnet
공식 코드TransformNet: Self-supervised representation learning through predicting geometric transformations
https://github.com/hasibzunair/msl-recognition
공식 코드Learning to recognize occluded and small objects with partial inputs
https://github.com/hasibzunair/synthetic-covid-cxr-dataset
공식 코드Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
https://github.com/hasibzunair/viptt-net
공식 코드https://github.com/hathawayxxh/CRCKD
공식 코드Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
https://github.com/hayyubi/cur_vl
공식 코드Learning from Children: Improving Image-Caption Pretraining via Curriculum
https://github.com/hazardfy/fiba
공식 코드FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
https://github.com/hazyresearch/safari
공식 코드Simple Hardware-Efficient Long Convolutions for Sequence Modeling
https://github.com/hazyresearch/state-spaces
공식 코드Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
https://github.com/hchautran/PiToMe
공식 코드Accelerating Transformers with Spectrum-Preserving Token Merging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.