{"url":"/method/convnext","slug":"convnext","name":"ConvNeXt","full_name":"ConvNeXt","full_name_withheld":false,"description_markdown":null,"description_state":"absent","introduced_year":null,"introduced_by":{"title":"A ConvNet for the 2020s","paper":"/paper/a-convnet-for-the-2020s","first_author":"Zhuang Liu","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/a-convnet-for-the-2020s"},"source":{"url":"https://arxiv.org/abs/2201.03545v2","title":"A ConvNet for the 2020s","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Backbone Architectures","url":"/methods/category/backbone-architectures","pwc_aliases":[]}],"n_papers_tagged":165,"archive_num_papers":165,"papers_newest_first":[{"paper":"/paper/spartan-spatial-reinforcement-token-based","title":"SpaRTAN: Spatial Reinforcement Token-based Aggregation Network for Visual Recognition","date":"2025-07-15","arxiv_id":"2507.10999","n_code_links":1,"syntology":null},{"paper":null,"title":"Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography","date":"2025-06-16","arxiv_id":"2506.13964","n_code_links":0,"syntology":null},{"paper":null,"title":"Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks","date":"2025-06-11","arxiv_id":"2506.09593","n_code_links":0,"syntology":null},{"paper":"/paper/rgc-bent-a-novel-dataset-for-bent-radio","title":"RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification","date":"2025-05-25","arxiv_id":"2505.19249","n_code_links":1,"syntology":null},{"paper":null,"title":"Deep Learning for Breast Cancer Detection: Comparative Analysis of ConvNeXT and EfficientNet","date":"2025-05-24","arxiv_id":"2505.18725","n_code_links":0,"syntology":null},{"paper":null,"title":"Achieving 3D Attention via Triplet Squeeze and Excitation Block","date":"2025-05-09","arxiv_id":"2505.05943","n_code_links":0,"syntology":null},{"paper":"/paper/foundation-models-for-seismic-data-processing","title":"Foundation Models For Seismic Data Processing: An Extensive Review","date":"2025-03-31","arxiv_id":"2503.24166","n_code_links":1,"syntology":null},{"paper":null,"title":"SupertonicTTS: Towards Highly Scalable and Efficient Text-to-Speech System","date":"2025-03-29","arxiv_id":"2503.23108","n_code_links":0,"syntology":null},{"paper":null,"title":"Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning","date":"2025-03-28","arxiv_id":"2503.22069","n_code_links":0,"syntology":null},{"paper":"/paper/surg-3m-a-dataset-and-foundation-model-for","title":"Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings","date":"2025-03-25","arxiv_id":"2503.19740","n_code_links":1,"syntology":null},{"paper":"/paper/frequency-dynamic-convolution-for-dense-image","title":"Frequency Dynamic Convolution for Dense Image Prediction","date":"2025-03-24","arxiv_id":"2503.18783","n_code_links":1,"syntology":{"ran":0,"of":2,"unverified":2,"pointer_only":0}},{"paper":null,"title":"Solution for 8th Competition on Affective & Behavior Analysis in-the-wild","date":"2025-03-14","arxiv_id":"2503.11115","n_code_links":0,"syntology":null},{"paper":null,"title":"Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)","date":"2025-03-04","arxiv_id":"2503.02241","n_code_links":0,"syntology":null},{"paper":null,"title":"CAE-Net: Generalized Deepfake Image Detection using Convolution and Attention Mechanisms with Spatial and Frequency Domain Features","date":"2025-02-15","arxiv_id":"2502.10682","n_code_links":0,"syntology":null},{"paper":null,"title":"AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach","date":"2025-02-07","arxiv_id":"2502.04682","n_code_links":0,"syntology":null},{"paper":null,"title":"DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions","date":"2025-02-07","arxiv_id":"2502.05091","n_code_links":0,"syntology":null},{"paper":"/paper/a-retrospective-systematic-study-on","title":"A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma","date":"2025-02-06","arxiv_id":"2502.03772","n_code_links":1,"syntology":null},{"paper":"/paper/learnable-polynomial-trigonometric-and","title":"Polynomial, trigonometric, and tropical activations","date":"2025-02-03","arxiv_id":"2502.01247","n_code_links":1,"syntology":null},{"paper":"/paper/iformer-integrating-convnet-and-transformer","title":"iFormer: Integrating ConvNet and Transformer for Mobile Application","date":"2025-01-26","arxiv_id":"2501.15369","n_code_links":1,"syntology":{"ran":5,"of":10,"unverified":5,"pointer_only":1}},{"paper":"/paper/enhancing-kelp-forest-detection-in-remote","title":"Enhancing kelp forest detection in remote sensing images using crowdsourced labels with Mixed Vision Transformers and ConvNeXt segmentation models","date":"2025-01-23","arxiv_id":"2501.14001","n_code_links":1,"syntology":null},{"paper":"/paper/a-cnn-transformer-for-classification-of","title":"A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction","date":"2025-01-18","arxiv_id":"2501.10733","n_code_links":1,"syntology":null},{"paper":null,"title":"Detection of Vascular Leukoencephalopathy in CT Images","date":"2025-01-16","arxiv_id":"2501.09863","n_code_links":0,"syntology":null},{"paper":"/paper/emonext-an-adapted-convnext-for-facial-1","title":"EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition","date":"2025-01-14","arxiv_id":"2501.08199","n_code_links":1,"syntology":null},{"paper":null,"title":"Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions","date":"2025-01-12","arxiv_id":"2501.06787","n_code_links":0,"syntology":null},{"paper":null,"title":"nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images Segmentation","date":"2025-01-02","arxiv_id":"2501.01406","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-deep-learning-approach-for-facial","title":"A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease","date":"2024-12-30","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach","date":"2024-12-27","arxiv_id":"2412.19950","n_code_links":0,"syntology":null},{"paper":null,"title":"Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation","date":"2024-12-20","arxiv_id":"2412.15924","n_code_links":0,"syntology":null},{"paper":"/paper/samudra-an-ai-global-ocean-emulator-for","title":"Samudra: An AI Global Ocean Emulator for Climate","date":"2024-12-05","arxiv_id":"2412.03795","n_code_links":1,"syntology":{"ran":0,"of":10,"unverified":10,"pointer_only":0}},{"paper":"/paper/artbrain-an-explainable-end-to-end-toolkit","title":"ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style","date":"2024-12-02","arxiv_id":"2412.01512","n_code_links":1,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":32},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":31},{"task":"/task/image-classification","name":"image-classification","papers":23},{"task":"/task/object-detection","name":"Object Detection","papers":19},{"task":"/task/classification-1","name":"Classification","papers":15},{"task":"/task/object-detection-1","name":"object-detection","papers":13},{"task":"/task/decoder","name":"Decoder","papers":10},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":8},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":8},{"task":"/task/segmentation","name":"Segmentation","papers":8},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":7},{"task":"/task/deep-learning","name":"Deep Learning","papers":6},{"task":"/task/diagnostic","name":"Diagnostic","papers":6},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":6},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":5},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":5},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":5},{"task":"/task/action-recognition-in-videos","name":"Action Recognition","papers":4},{"task":"/task/decision-making","name":"Decision Making","papers":4},{"task":"/task/diversity","name":"Diversity","papers":4}],"tasks_shown":20,"n_tasks":174,"usage_by_year":[{"year":"2022","papers":35},{"year":"2023","papers":44},{"year":"2024","papers":61},{"year":"2025","papers":25}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/convnext"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}