Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

arXiv:2604.25188 · cs.CV · Submitted 2026-04-28 · Read on arXiv

cs.CV

Submitted: 2026-04-28

Updated: 2026-04-28

Terminology

• Feature Extraction — 25× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Attention Mechanism — 7× in this paper · explained in 3 episodes
• Context Excitation — 7× in this paper
• Regularization — 7× in this paper · explained in 3 episodes
• Multi-Branch Random Dilated Convolution — 5× in this paper
• Fine-Grained Feature Enhancement — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Global Average Pooling — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Criss-Cross Attention — 2× in this paper
• Generalizability — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Gradient Flow — 2× in this paper · explained in 2 episodes
• Hybrid Architectures — 2× in this paper · explained in 3 episodes
• Non-Local Block — 2× in this paper
• Pruning — 2× in this paper · explained in 4 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Atrous Spatial Pyramid Pooling — 1× in this paper
• Benchmarking — 1× in this paper · explained in 4 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Global Context Block — 1× in this paper
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Street View House Numbers — 1× in this paper
• Wide Residual Networks — 1× in this paper

Sources

• Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks
• Rethinking Attention with Performers
• Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting
• UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• TextSquare: Scaling up Text-Centric Visual Instruction Tuning
• Vision as LoRA
• Multi-Scale Context Aggregation by Dilated Convolutions
• Wide Residual Networks
• QKFormer: Hierarchical Spiking Transformer using Q-K Attention
• TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering

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