LiAuto-MindViT: A Hybrid Vision Backbone with Adaptive Bidirectional Mamba

arXiv:2609.24337 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-22

Terminology

• Transformer — 37× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• State Space Model — 11× in this paper
• alpha — 10× in this paper · explained in 1 episode
• Adaptive Bidirectional Mamba — 9× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Aggregation — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Local Extraction Network — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Conditional Position Encoding — 2× in this paper
• Convolutional Neural Network — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Awareness — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Geometric Mismatch — 1× in this paper · explained in 2 episodes
• Hierarchical Framework — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mamba-Transformer-based Global Perception Network — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• Conditional Positional Encodings for Vision Transformers
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Efficiently Modeling Long Sequences with Structured State Spaces
• FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining
• Simplified State Space Layers for Sequence Modeling
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• EventGait: Towards Robust Gait Recognition with Event Streams
• Falcon Mamba: The First Competitive Attention-free 7B Language Model

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models