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/dsgt-kaggle-clef/plantclef-2024
공식 코드Multi-Label Plant Species Classification with Self-Supervised Vision Transformers
https://github.com/dsshim0125/gaussian-ram
공식 코드https://github.com/dstamoulis/single-path-nas
공식 코드Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/dtennant/dt-tradeoff
공식 코드Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
https://github.com/duanzhiihao/edge-cloud-rac
공식 코드https://github.com/ducha-aiki/LSUVinit
공식 코드https://github.com/ducha-aiki/caffenet-benchmark
공식 코드FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
https://github.com/duchenzhuang/flowencrypt
공식 코드https://github.com/dun-research/haltingvt
공식 코드HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
https://github.com/dustincarrion/cd-metadl
공식 코드Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
https://github.com/dvirsamuel/DRAGON
공식 코드From Generalized zero-shot learning to long-tail with class descriptors
https://github.com/dvirsamuel/SeedSelect
공식 코드Norm-guided latent space exploration for text-to-image generation
https://github.com/dvlab-research/logits-based-finetuning
공식 코드https://github.com/dvlab-research/minigemini
공식 코드Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/dvlab-research/parametric-contrastive-learning
공식 코드https://github.com/dvlab-research/rescom
공식 코드Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
https://github.com/dvornikita/SUR
공식 코드Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
https://github.com/dvssajay/New_World
공식 코드ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities
https://github.com/dwang181/selectivecal
공식 코드On Calibrating Semantic Segmentation Models: Analyses and An Algorithm
https://github.com/dwromero/ckconv
공식 코드https://github.com/dxaen/percival
공식 코드Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
https://github.com/dyabel/detpro
공식 코드Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
https://github.com/dydjw9/RAIN
공식 코드RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
https://github.com/dynamical-inference/patchsae
공식 코드Sparse autoencoders reveal selective remapping of visual concepts during adaptation
https://github.com/dystopians/secdood
공식 코드Secure On-Device Video OOD Detection Without Backpropagation
https://github.com/dyunis/spectral_dynamics
공식 코드Approaching Deep Learning through the Spectral Dynamics of Weights
https://github.com/dywu98/SIRFP
공식 코드Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation
https://github.com/dzp2095/fedlsm
공식 코드Scale Federated Learning for Label Set Mismatch in Medical Image Classification
https://github.com/easezyc/deep-transfer-learning
공식 코드https://github.com/easonxiao-888/grootvl
공식 코드https://github.com/ebagdasa/differential-privacy-vs-fairness
공식 코드https://github.com/ebennequin/FewShotVision
공식 코드https://github.com/ebennequin/few-shot-open-set
공식 코드https://github.com/eclique/RISE
공식 코드RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/ecs-vlc/FMix
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.