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新论文 · v1arXiv:2611.17077

SALT: State-Aligned Low-Rank Transfer for Single-Temporal Cross-Dataset Structural Localization in Remote Sensing

Aggelos Psiris, Georgios Angelidis, Giorgos Sfikas, Maria Vakalopoulou, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos

原文摘要

Change detection in remote sensing conventionally relies on spatially aligned bi-temporal imagery, which may be unavailable or unreliable in practical scenarios. Recent single-temporal approaches address this limitation by learning structural evidence, such as building regions, from a labeled source dataset and transferring it to unseen target imagery using only the current image. However, adaptation to the source dataset can over-specialize pretrained features and reduce their cross-dataset generalization, a risk that this paper argues is higher when the feature adaptation is parameterized independently of the states used for prediction. To address this issue, the current study introduces SALT, a State-Aligned Low-Rank Transfer framework. SALT keeps a DINOv3 backbone frozen and learns negative and positive structural states that both score each patch and, through State-Aligned Low-Rank Adaptation (SALA), span the rank-2 subspace in which patch features are corrected. Directional State Alignment supervises these corrections to raise the evidence of positive patches and lower that of negative ones, while Geometry Preservation limits changes to the pairwise relations among pretrained patch features. Experiments on LEVIR-CD and the WHU aerial building dataset show that, at the pixel level and in both transfer directions, SALT outperforms four baselines reproduced under a common source-only protocol, improving over the strongest reproduced baselines by 9.45 and 5.79 points in pixel-level AP and by 27.69 and 14.25 points in IoU (LEVIR-CD to WHU and WHU to LEVIR-CD, respectively), with only 0.068M trainable parameters and 40.2 ms per image. In order to facilitate reproducibility and further research, the source code of SALT is available at https://github.com/gthpapadopoulos/SALT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.08612

Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions

Spyridon Georgiou, Aggelos Psiris, Spyridon Evangelatos, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos

原文摘要

Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers to deep convolutional, attention-based, vision-language, and foundation-model architectures, and by the parallel growth of large-scale in-the-wild benchmarks spanning categorical, dimensional, compound, micro-expression, Action Unit (AU), and intensity-estimation tasks. Yet the deep learning-based FER landscape has so far been reviewed only along narrow task-, architecture-, or application-specific axes, leaving a holistic, systematically organized account of its recent advances missing. This survey addresses that gap with a comprehensive review of recent deep learning-based FER, explicitly linked to the wider Facial Affect Recognition (FAR) domain. Its main contributions are: a) A description of FER's evolution into five distinct phases, from handcrafted features and classical machine learning to attention-based, vision-language, and foundation-model approaches, with the key milestone works of each, b) A multi-criteria taxonomy analyzing the literature along seven complementary axes: recognition task, input modality, face pre-processing pipeline, network architecture, learning strategy, acquisition setting, and application domain, c) A per-criterion comparative analysis, with critical insights into the strengths and limitations of each category under in-the-wild conditions, d) A task-organized review of public FER datasets, with their annotation schemes, modalities, and evaluation protocols, e) A compilation of performance metrics and a per-task quantitative comparison of representative state-of-the-art methods on widely adopted benchmarks, and f) A discussion of current challenges and promising future directions.

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15300

GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection

Aggelos Psiris, Yannis Panagakis, Maria Vakalopoulou, Georgios Th. Papadopoulos

原文摘要

Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) is a critical task in modern manufacturing, where automated product inspection systems must identify rare defects using only a handful of normal, defect-free training samples. Existing approaches typically model normality by assessing each patch largely through its individual appearance, e.g., through similarity to stored normal features, reconstruction from support samples, or distance from a learned normal representation. This paper introduces GATE-AD, whose key contribution is to instead model few-shot normality through local contextual relations: a patch is considered normal when it can be consistently recovered from its learned dependencies with its spatial neighbors. To realize this formulation, frozen self-supervised ViT patch tokens are organized on a spatial neighbor graph and processed by a Graph Attention Network (GAT), while representation alignment and a Scaled Cosine Error (SCE) objective enable reconstruction-based anomaly scoring. Node masking and attention dropout further regularize learning in the low-shot regime. On MVTec AD, VisA, and MPDD, GATE-AD achieves strong detection and localization performance across $1$- to $8$-shot settings, attaining the highest image-level AUROC in $10$ of $12$ settings and the highest image-level AP in $8$ of $12$ settings under a unified evaluation protocol, while also achieving the lowest inference latency among the compared methods. In order to facilitate reproducibility and further research, the source code of GATE-AD is available at https://github.com/gthpapadopoulos/GATE-AD.

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arXiv ↗PDF ↗