VisionHOPE: Visual Backbones as Self-Modifying Learning Systems

arXiv:2609.33325 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Transformer — 24× in this paper · explained in 2 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 11× in this paper
• Aggregation — 10× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Adaptation — 9× in this paper · explained in 1 episode
• Associative Memory — 8× in this paper · explained in 2 episodes
• Nested Learning — 7× in this paper
• Instance Segmentation — 6× in this paper · explained in 3 episodes
• European Conference on Computer Vision — 5× in this paper
• Test-Time Training — 4× in this paper
• International Conference on Learning Representations — 3× in this paper
• International Conference on Machine Learning — 3× in this paper
• Convolutional Neural Networks (CNNs) — 2× in this paper · explained in 2 episodes
• Effective Receptive Field — 2× in this paper
• Hypernetwork — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Continuum Memory System — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Feed-Forward Network — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
• 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
• Vision-TTT: Efficient and Expressive Visual Representation Learning with Test-Time Training
• Vision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics
• ViT-5: Vision Transformers for The Mid-2020s
• PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
• LBMamba: Locally Bi-directional Mamba

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