arXiv Computer Vision
Sep 10

Layerwise Tunable Lifting Scheme for the Convolutional Neural Network

The paper presents a family of tunable lifting schemes for biorthogonal wavelet filter banks, introducing three strategies: low‑pass tuning, high‑pass tuning, and a sequential scheme that jointly adapts both branches. These designs use a lattice‑based lifting structure to ensure invertibility and stability for any parameter values. The authors integrated the schemes into a ResNet‑18 backbone and evaluated them on image classification (DTD) and anomaly detection (MVTec‑AD and KRC102S), reporting consistent performance improvements across all tasks.

By Abdumannon Yovkochov, An Le, Sungbal Seo, You-Suk Bae, Truong Nguyen
arXiv Computer Vision
Sep 24

Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification

The paper introduces SGFNet, a Semantic‑Guided Fusion Network for classifying multi‑source remote sensing images. It features a Semantic Mixing Convolution Block that generates semantic‑aware kernels based on contextual relationships, and a Frequency Modulated Fusion Block that fuses cross‑modal information in the frequency domain to mitigate spatial misalignment. Experiments on the Augsburg and Houston 2018 datasets show SGFNet consistently outperforms state‑of‑the‑art methods.

By Yuwei Zhao, Chuanzheng Gong, Baogui Huan, Feng Gao, Junyu Dong, Qian Du
arXiv Computer Vision
Sep 11

HiPerViT: A Hierarchical Perceiver-Vision Transformer Architecture for Multi-Scale Texture Recognition

HiPerViT is a compact vision-only architecture that injects an explicit second-order statistical prior into a transformer-based pipeline for texture recognition. It combines global and local image views with a compact bilinear descriptor encoded as a statistical token, and integrates this token with first-order spatial representations through Perceiver-style latent distillation. Across six texture recognition benchmarks, HiPerViT consistently outperforms strong vision-only baselines, achieving notable gains on DTD, GTOS-Mobile, and 1200Tex, and the improvements are largely independent of backbone depth or fusion topology.

By Jo\~ao Pedro C. A. de S\'a, Odemir Martinez Bruno