{"total":80,"rows":[{"id":"2203.04967","title":"UNeXt: MLP-based Rapid Medical Image Segmentation Network","created":"2022-03-09","cited_by_count":185,"area":null},{"id":"2204.03645","title":"DaViT: Dual Attention Vision Transformers","created":"2022-04-07","cited_by_count":175,"area":null},{"id":"2306.09364","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","created":"2023-06-14","cited_by_count":139,"area":null},{"id":"2107.10224","title":"CycleMLP: A MLP-like Architecture for Dense Prediction","created":"2021-07-21","cited_by_count":126,"area":null},{"id":"2107.08391","title":"AS-MLP: An Axial Shifted MLP Architecture for Vision","created":"2021-07-18","cited_by_count":101,"area":null},{"id":"2211.11943","title":"Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition","created":"2022-11-22","cited_by_count":81,"area":null},{"id":"2111.11187","title":"PointMixer: MLP-Mixer for Point Cloud Understanding","created":"2021-11-22","cited_by_count":71,"area":null},{"id":"2206.10810","title":"A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift","created":"2022-06-22","cited_by_count":68,"area":null},{"id":"2108.13341","title":"Hire-MLP: Vision MLP via Hierarchical Rearrangement","created":"2021-08-30","cited_by_count":66,"area":null},{"id":"2111.04060","title":"Are we ready for a new paradigm shift? A Survey on Visual Deep MLP","created":"2021-11-07","cited_by_count":63,"area":null},{"id":"2111.12294","title":"An Image Patch is a Wave: Phase-Aware Vision MLP","created":"2021-11-24","cited_by_count":61,"area":null},{"id":"2406.00123","title":"Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration","created":"2024-05-31","cited_by_count":38,"area":null},{"id":"2109.04454","title":"ConvMLP: Hierarchical Convolutional MLPs for Vision","created":"2021-09-09","cited_by_count":37,"area":null},{"id":"2207.13159","title":"TINYCD: A (Not So) Deep Learning Model For Change Detection","created":"2022-07-26","cited_by_count":36,"area":null},{"id":"2206.01136","title":"Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives","created":"2022-06-02","cited_by_count":33,"area":null},{"id":"2112.11081","title":"RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality","created":"2021-12-21","cited_by_count":32,"area":null},{"id":"2201.10801","title":"When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism","created":"2022-01-26","cited_by_count":30,"area":null},{"id":"2201.12083","title":"DynaMixer: A Vision MLP Architecture with Dynamic Mixing","created":"2022-01-28","cited_by_count":28,"area":null},{"id":"2108.01072","title":"S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision","created":"2021-08-02","cited_by_count":24,"area":null},{"id":"2205.01972","title":"Sequencer: Deep LSTM for Image Classification","created":"2022-05-04","cited_by_count":23,"area":null},{"id":"2111.12527","title":"MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning","created":"2021-11-24","cited_by_count":21,"area":null},{"id":"2203.14679","title":"Brain-inspired Multilayer Perceptron with Spiking Neurons","created":"2022-03-28","cited_by_count":16,"area":null},{"id":"2106.14882","title":"Rethinking Token-Mixing MLP for MLP-based Vision Backbone","created":"2021-06-28","cited_by_count":15,"area":null},{"id":"2203.03691","title":"HyperMixer: An MLP-based Low Cost Alternative to Transformers","created":"2022-03-07","cited_by_count":13,"area":null},{"id":"2201.13027","title":"BOAT: Bilateral Local Attention Vision Transformer","created":"2022-01-31","cited_by_count":12,"area":null},{"id":"2401.17992","title":"Multilinear Operator Networks","created":"2024-01-31","cited_by_count":12,"area":null},{"id":"2408.08338","title":"Activation Space Selectable Kolmogorov-Arnold Networks","created":"2024-08-15","cited_by_count":9,"area":null},{"id":"2407.13108","title":"UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt","created":"2024-07-18","cited_by_count":8,"area":null},{"id":"2112.04453","title":"MLP Architectures for Vision-and-Language Modeling: An Empirical Study","created":"2021-12-08","cited_by_count":6,"area":null},{"id":"2202.06510","title":"Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs","created":"2022-02-14","cited_by_count":6,"area":null},{"id":"2203.06108","title":"Active Token Mixer","created":"2022-03-11","cited_by_count":6,"area":null},{"id":"2209.01404","title":"Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies","created":"2022-09-03","cited_by_count":6,"area":null},{"id":"2302.08374","title":"Efficiency 360: Efficient Vision Transformers","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2304.10054","title":"Complex Mixer for MedMNIST Classification Decathlon","created":"2023-04-20","cited_by_count":6,"area":null},{"id":"2311.16707","title":"Full-resolution MLPs Empower Medical Dense Prediction","created":"2023-11-28","cited_by_count":6,"area":null},{"id":"2108.04384","title":"RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?","created":"2021-08-09","cited_by_count":5,"area":null},{"id":"2208.11718","title":"gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window","created":"2022-08-24","cited_by_count":5,"area":null},{"id":"2209.06383","title":"Analysis of Quantization on MLP-based Vision Models","created":"2022-09-14","cited_by_count":5,"area":null},{"id":"2501.14970","title":"AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges","created":"2025-01-24","cited_by_count":5,"area":null},{"id":"2502.09928","title":"Deep Tree Tensor Networks","created":"2025-02-14","cited_by_count":5,"area":null},{"id":"2505.04165","title":"TS-SNN: Temporal Shift Module for Spiking Neural Networks","created":"2025-05-07","cited_by_count":5,"area":null},{"id":"2107.02174","title":"What Makes for Hierarchical Vision Transformer?","created":"2021-07-05","cited_by_count":4,"area":null},{"id":"2111.13156","title":"Global Interaction Modelling in Vision Transformer via Super Tokens","created":"2021-11-25","cited_by_count":4,"area":null},{"id":"2205.09120","title":"Fast matrix multiplication for binary and ternary CNNs on ARM CPU","created":"2022-05-18","cited_by_count":4,"area":null},{"id":"2305.17644","title":"Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation","created":"2023-05-28","cited_by_count":4,"area":null},{"id":"2310.02513","title":"A Recipe for Improved Certifiable Robustness","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2406.08298","title":"AdaNCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer","created":"2024-06-12","cited_by_count":4,"area":null},{"id":"2108.07505","title":"MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation","created":"2021-08-17","cited_by_count":3,"area":null},{"id":"2201.00978","title":"PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture","created":"2022-01-04","cited_by_count":3,"area":null},{"id":"2202.08456","title":"MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition","created":"2022-02-17","cited_by_count":3,"area":null},{"id":"2212.12552","title":"A Close Look at Spatial Modeling: From Attention to Convolution","created":"2022-12-23","cited_by_count":3,"area":null},{"id":"2407.02934","title":"PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video Recognition","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2507.02151","title":"Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks","created":"2025-07-02","cited_by_count":3,"area":null},{"id":"2211.11085","title":"R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition","created":"2022-11-20","cited_by_count":2,"area":null},{"id":"2312.00412","title":"SCHEME: Scalable Channel Mixer for Vision Transformers","created":"2023-12-01","cited_by_count":2,"area":null},{"id":"2402.02377","title":"NOAH: Learning Pairwise Object Category Attentions for Image Classification","created":"2024-02-04","cited_by_count":2,"area":null},{"id":"2403.17445","title":"Incorporating Exponential Smoothing into MLP: A Simple but Effective Sequence Model","created":"2024-03-26","cited_by_count":2,"area":null},{"id":"2410.10914","title":"Towards Better Multi-head Attention via Channel-wise Sample Permutation","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2411.07118","title":"ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition","created":"2024-11-11","cited_by_count":2,"area":null},{"id":"2503.17193","title":"MSCA-Net:Multi-Scale Context Aggregation Network for Infrared Small Target Detection","created":"2025-03-21","cited_by_count":2,"area":null},{"id":"2505.03113","title":"Image Recognition with Online Lightweight Vision Transformer: A Survey","created":"2025-05-06","cited_by_count":2,"area":null},{"id":"2207.07284","title":"Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP","created":"2022-07-15","cited_by_count":1,"area":null},{"id":"2207.08265","title":"MLP-GAN for Brain Vessel Image Segmentation","created":"2022-07-17","cited_by_count":1,"area":null},{"id":"2208.11108","title":"Efficient Attention-free Video Shift Transformers","created":"2022-08-23","cited_by_count":1,"area":null},{"id":"2303.07726","title":"Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion","created":"2023-03-14","cited_by_count":1,"area":null},{"id":"2303.13504","title":"ReBotNet: Fast Real-time Video Enhancement","created":"2023-03-23","cited_by_count":1,"area":null},{"id":"2304.03532","title":"Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction","created":"2023-04-07","cited_by_count":1,"area":null},{"id":"2304.12615","title":"STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation","created":"2023-04-25","cited_by_count":1,"area":null},{"id":"2305.14675","title":"TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2311.13263","title":"CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning","created":"2023-11-22","cited_by_count":1,"area":null},{"id":"2603.05756","title":"Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2208.08191","title":"Transformer Vs. MLP-Mixer: Exponential Expressive Gap For NLP Problems","created":"2022-08-17","cited_by_count":0,"area":null},{"id":"2308.11204","title":"A Simple Framework for Multi-mode Spatial-Temporal Data Modeling","created":"2023-08-22","cited_by_count":0,"area":null},{"id":"2308.13363","title":"CS-Mixer: A Cross-Scale Vision MLP Model with Spatial-Channel Mixing","created":"2023-08-25","cited_by_count":0,"area":null},{"id":"2309.06721","title":"Dynamic Spectrum Mixer for Visual Recognition","created":"2023-09-13","cited_by_count":0,"area":null},{"id":"2402.18007","title":"Mixer is more than just a model","created":"2024-02-28","cited_by_count":0,"area":null},{"id":"2405.16098","title":"Lateralization MLP: A Simple Brain-inspired Architecture for Diffusion","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2411.12547","title":"S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation","created":"2024-11-19","cited_by_count":0,"area":null},{"id":"2503.02534","title":"SAGE-Amine: Generative Amine Design with Multi-Property Optimization for Efficient CO2 Capture","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2609.20181","title":"MoSSGate: Memory-Modulated State-Space Gating for Skin Lesion Segmentation","created":"2026-07-24","cited_by_count":0,"area":null}],"next_cursor":null}