EVCC: Enhanced Vision Transformer-ConvNeXt-CoAtNet Fusion for Classification

• URL: http://arxiv.org/abs/2511.18691v1
• Date: Mon, 24 Nov 2025 02:11:19 GMT
• Title: EVCC: Enhanced Vision Transformer-ConvNeXt-CoAtNet Fusion for Classification
• Authors: Kazi Reyazul Hasan, Md Nafiu Rahman, Wasif Jalal, Sadif Ahmed, Shahriar Raj, Mubasshira Musarrat, Muhammad Abdullah Adnan,
• Abstract summary: Hybrid vision architectures combining Transformers and CNNs have significantly advanced image classification, but they usually do so at significant computational cost.<n>We introduce EVCC, a novel multi-branch architecture integrating the Vision Transformer, lightweight ConvNeXt, and CoAtNet.<n> Experiments across the CIFAR-100, Tobacco3482, CelebA, and Brain Cancer datasets demonstrate EVCC's superiority over powerful models.
• Score: 0.5394291557377919
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hybrid vision architectures combining Transformers and CNNs have significantly advanced image classification, but they usually do so at significant computational cost. We introduce EVCC (Enhanced Vision Transformer-ConvNeXt-CoAtNet), a novel multi-branch architecture integrating the Vision Transformer, lightweight ConvNeXt, and CoAtNet through key innovations: (1) adaptive token pruning with information preservation, (2) gated bidirectional cross-attention for enhanced feature refinement, (3) auxiliary classification heads for multi-task learning, and (4) a dynamic router gate employing context-aware confidence-driven weighting. Experiments across the CIFAR-100, Tobacco3482, CelebA, and Brain Cancer datasets demonstrate EVCC's superiority over powerful models like DeiT-Base, MaxViT-Base, and CrossViT-Base by consistently achieving state-of-the-art accuracy with improvements of up to 2 percentage points, while reducing FLOPs by 25 to 35%. Our adaptive architecture adjusts computational demands to deployment needs by dynamically reducing token count, efficiently balancing the accuracy-efficiency trade-off while combining global context, local details, and hierarchical features for real-world applications. The source code of our implementation is available at https://anonymous.4open.science/r/EVCC.

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