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.
arXiv:2506.24092v3 Announce Type: replace Abstract: Adapting large pretrained vision models to medical image classification is often limited by memory, computation, and task-specific specializations....
arXiv:2601.06834v2 Announce Type: replace Abstract: Low-resolution image representation can be regarded as a special form of sparse representation that retains only low-frequency information while di...
WaveScat introduces a new family of feature extractors for speech deepfake detection that merges wavelet scattering transform (WST) with self‑supervised learning. By cascading wavelet convolutions with modulus nonlinearities, WaveScat produces deformation‑stable, multi‑scale features that capture both low‑level spectral details and higher‑level information. Experiments on Deepfake‑Eval‑2024 and cross‑dataset tests (SpoofCeleb, In‑the‑Wild, ASVspoof 5) demonstrate that WaveScat significantly outperforms existing front‑ends, with a small averaging scale and high‑frequency directional resolution proving crucial for detecting subtle artifacts.
arXiv:2608.28524v1 Announce Type: new Abstract: Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote...
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
arXiv:2607. 13043v1 Announce Type: cross Abstract: Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands.
arXiv:2607. 03653v1 Announce Type: cross Abstract: Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats.
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
arXiv:2505. 18587v2 Announce Type: replace-cross Abstract: Deepfakes pose a significant threat to digital media security, with current detection methods struggling to generalize across different manipulation techniques and datasets.
arXiv:2607. 08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions.
GradAttn replaces fixed residual connections in deep ConvNets with attention‑controlled gradient pathways, allowing the network to dynamically weight shallow texture features and deep semantic representations. The method extracts multi‑scale CNN features at different depths and regulates them through self‑attention, leading to improved performance over ResNet‑18 on five of eight evaluated datasets, including a +11.07% accuracy gain on FashionMNIST. Analysis of gradient flow shows that controlled instabilities introduced by attention can coincide with better generalization, while positional encoding proves to be dataset‑dependent.
Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples.