https://github.com/microsoft/geospatial-ood-detection
공식 코드Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/microsoft/mlc
공식 코드https://github.com/microsoft/muzic/tree/main/musicbert
공식 코드MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training
https://github.com/microsoft/react
공식 코드Learning Customized Visual Models with Retrieval-Augmented Knowledge
https://github.com/microsoft/regionclip
공식 코드https://github.com/microsoft/semi-supervised-learning
공식 코드FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
https://github.com/microsoft/seqsnn
공식 코드Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
https://github.com/microsoft/simmim
공식 코드https://github.com/microsoft/unicl
공식 코드https://github.com/microsoft/unilm
공식 코드LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/microsoft/unilm/tree/master/beit
공식 코드https://github.com/microsoft/unilm/tree/master/beit2
공식 코드BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
https://github.com/microsoft/unilm/tree/master/dit
공식 코드DiT: Self-supervised Pre-training for Document Image Transformer
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/unilm/tree/master/layoutxlm
공식 코드LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/microsoft/vision-longformer
공식 코드Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/mihirp1998/Slot-TTA
공식 코드https://github.com/mikehuisman/lstm-fewshotlearning-oplstm
공식 코드https://github.com/mikehuisman/revisiting-learned-optimizers
공식 코드https://github.com/mikehuisman/subspace-adaptation-prior
공식 코드https://github.com/mikelzc1990/nsganetv2
공식 코드Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
https://github.com/mil-tokyo/MCD_DA
공식 코드Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/mil-tokyo/bc_learning_image
공식 코드https://github.com/miladmozafari/predify
공식 코드Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
https://github.com/mileyan/simple_shot
공식 코드SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
https://github.com/millerk22/rwll_active_learning
공식 코드Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege
공식 코드Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge
https://github.com/mindspore-ai/models/tree/master/research/cv/ISyNet
공식 코드ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/mindspore-lab/models
공식 코드PPT: Token Pruning and Pooling for Efficient Vision Transformers
https://github.com/mingkai-zheng/ReSSL
공식 코드Weak Augmentation Guided Relational Self-Supervised Learning
https://github.com/mingkai-zheng/cone
공식 코드CoNe: Contrast Your Neighbours for Supervised Image Classification
https://github.com/mingkai-zheng/simmatchv2
공식 코드https://github.com/minglllli/CLS-RL
공식 코드Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning
https://github.com/mingzeg/moment-probing
공식 코드https://github.com/minhanh151/pre
공식 코드PRE: Vision-Language Prompt Learning with Reparameterization Encoder
https://github.com/minhanh151/respro
공식 코드PRE: Vision-Language Prompt Learning with Reparameterization Encoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.