SOTA2 Research · papers

Papers for “image classification”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

Search results

20 indexed

Title	Authors	Year	Source
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures	Masanori Suganuma, Shinichi Shirakawa, Tomoharu Nagao	2017	arxiv 1704.00764
Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data	Zhipeng Chang, Ting He, Wenrui Hao	2026	arxiv 2601.13608
Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models	Katharina Hechinger, Xiao Xiang Zhu, Göran Kauermann	2023	arxiv 2309.01440
A Separable Self-attention Inspired by the State Space Model for Computer Vision	Juntao Zhang, Shaogeng Liu, Kun Bian	2025	arxiv 2501.02040
V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models	Guilherme Vieira Neto, Marcos Eduardo Valle	2025	arxiv 2505.05659
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks	Rajat Saini, Nandan Kumar Jha, Bedanta Das	2020	arxiv 2006.15102
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework	Lamei Zhang, Hongwei Dong, Bin Zou	2019	arxiv 1903.09917
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks	Alessandro Sebastianelli, Francesco Mauro, Giulia Ciabatti	2024	arxiv 2407.17108
Bridge the Modality and Capability Gaps in Vision-Language Model Selection	Chao Yi, Yu-Hang He, De-Chuan Zhan	2024	arxiv 2403.13797
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models

The challenge of representation learning: Improved accuracy in deep vision models does not come with better predictions of perceptual similarity	Fritz Günther, Marco Marelli, Marco Alessandro Petilli	2023	arxiv 2303.07084