arXiv:2604. 16936v2 Announce Type: replace-cross Abstract: Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC).
arXiv:2609.03829v2 Announce Type: replace Abstract: Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critica...
The paper investigates few-shot fine-grained image classification and emphasizes the importance of phase information for capturing structural relationships. It introduces a plug‑and‑play amplitude‑phase integration (API) module that merges local and global frequency amplitude and phase data to create richer feature descriptors. A new network, PSF‑Net, adaptively fuses phase‑based spatial and frequency information and can be integrated into standard episodic training pipelines, achieving superior performance on five public datasets.
arXiv:2607.05176v3 Announce Type: replace Abstract: Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by r...
arXiv:2511. 10806v1 Announce Type: cross Abstract: Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake.
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
The paper introduces S$^3$F-Net, a dual‑branch network that fuses spatial and spectral representations for medical image classification. It combines a deep spatial CNN with a shallow spectral encoder, SpectraNet, which uses a learnable SpectralFilter layer to process the full Fourier spectrum efficiently. Evaluated on four medical imaging datasets, S$^3$F-Net consistently outperforms spatial‑only baselines, achieving state‑of‑the‑art accuracy on BRISC2025 and surpassing deeper models on the Chest X‑Ray Pneumonia dataset.
The paper introduces Modality‑Specific Frequency Distillation (MSFD), a continual learning framework for video deepfake detection that separates spatial, temporal, and spatiotemporal features in the frequency domain. By preserving each modality independently and applying a cross‑modality decorrelation loss, MSFD adapts to new forgery patterns while maintaining performance across diverse continual deepfake video scenarios. Experiments demonstrate that this approach outperforms state‑of‑the‑art methods in both adaptation and retention.
arXiv:2607. 17441v1 Announce Type: cross Abstract: Deepfake generation has raised growing concerns regarding digital media authenticity, misinformation, identity fraud, and public trust.
The paper introduces a Focal Log-Frequency Loss (f-loss) to counteract the spectral imbalance in pixel-space flow matching, where low frequencies dominate training. By balancing learning signals across frequencies and combining early frequency-domain supervision with later pixel-space refinement, the method accelerates convergence by up to 40% and improves FID and perceptual fidelity across multiple model scales. It requires no architectural changes and can replace existing flow matching losses as a drop‑in solution.
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.
arXiv:2606. 08204v1 Announce Type: new Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities.
arXiv:2606. 28226v1 Announce Type: cross Abstract: Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference.