Papers › Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule Attention
Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule Attention
Hasindu Dewasurendra, Taejoon Kim
Despite extensive applications in surveillance and remote sensing, research on very low-resolution (VLR) image classification remains relatively unexplored in comparison to high-resolution (HR) image classification. We introduce a deep hybrid network that integrates capsule routing networks with a two-layer attention module. In the proposed architecture, the attention mechanism captures the more salient features, and the capsule network encodes these features to be robust to resolution changes. To enhance the network’s performance, a transfer learning on a custom image dataset, which is well-aligned to CIFAR-10, is utilized. The proposed model (Codes for the models are available at: https://github.com/kdhasi/Deep-CapsuleAttention.git ) is evaluated on two VLR classification tasks of ‘VLR complex image’ and ‘VLR real-world digit’. Experimental results demonstrate the superiority of the proposed model, achieving state-of-the-art (SOTA) results in both VLR complex image and VLR real-world digit domains while using fewer parameters compared to previous SOTA networks. Specifically, on the VLR CIFAR-10 dataset, the proposed model attains a 3.17% improvement in detection accuracy over the current benchmarks, and, on the VLR SVHN dataset, it achieves a 3.85% improvement by using 80% fewer parameters.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections