https://github.com/shendu-sw/statistical-loss
공식 코드Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
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/shendu0321/incepformer
공식 코드IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation
https://github.com/shengcailiao/QAConv
공식 코드TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
https://github.com/shengliu66/ELR
공식 코드Early-Learning Regularization Prevents Memorization of Noisy Labels
https://github.com/shengliu66/sop
공식 코드https://github.com/shenzebang/centaur-privacy-federated-representation-learning
공식 코드https://github.com/shenzy08/PDO-eConvs
공식 코드PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
https://github.com/shi-labs/natten
공식 코드https://github.com/shifts-project/shifts
공식 코드Shifts 2.0: Extending The Dataset of Real Distributional Shifts
https://github.com/shihongl1998/llm-as-a-blackbox-optimizer
공식 코드Language Models as Black-Box Optimizers for Vision-Language Models
https://github.com/shihuihong214/trio-vit
공식 코드Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
https://github.com/shijiangming1/clip2fl
공식 코드CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data
https://github.com/shijing001/unicertainty_calibration_audio_classifiers
공식 코드https://github.com/shijxcs/lift
공식 코드Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
https://github.com/shijxcs/lift-plus
공식 코드https://github.com/shikunli/sel-cl
공식 코드https://github.com/shilinyan99/crosslmm
공식 코드CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms
https://github.com/shilonghong/dagnet
공식 코드DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security Inspection
https://github.com/shinypond/ood_detection
공식 코드Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models
https://github.com/shipra25jain/ESSNet
공식 코드Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
https://github.com/shirakawas/ASNG-NAS
공식 코드Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
https://github.com/shiranzada/pure-noise
공식 코드https://github.com/shivam-chandhok/cpsc-540-project
공식 코드Do Vision-Language Foundational models show Robust Visual Perception?
https://github.com/shiwt03/ssformer
공식 코드SSformer: A Lightweight Transformer for Semantic Segmentation
https://github.com/shlokk/hcl
공식 코드Hyperbolic Contrastive Learning for Visual Representations beyond Objects
https://github.com/shngjz/pmatch
공식 코드PMatch: Paired Masked Image Modeling for Dense Geometric Matching
https://github.com/shogi880/DPLCLIP
공식 코드Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains
https://github.com/showlab/sparseformer
공식 코드SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
https://github.com/shramanpramanick/transformer_based_geo-localization
공식 코드Where in the World is this Image? Transformer-based Geo-localization in the Wild
https://github.com/shravan-18/SAG-ViT
공식 코드SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers
https://github.com/shreyasharma99/cbgt-net
공식 코드CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
https://github.com/shrezaei/MI-Attack
공식 코드https://github.com/shrezaei/Target-Agnostic-Attack
공식 코드A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
https://github.com/shruti-jadon/Hands-on-One-Shot-Learning
공식 코드An Overview of Deep Learning Architectures in Few-Shot Learning Domain
https://github.com/shubhishukla10/parl
공식 코드https://github.com/shuguang99/DualAug
공식 코드DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.