Existing RGB-T salient object detection methods predominantly rely on manually aligned and annotated datasets, struggling to handle real-world scenarios with raw, unaligned RGB-T image pairs. In practical applications, due to significant cross-modal disparities such as spatial misalignment, scale variations, and viewpoint shifts, the performance of current methods drastically deteriorates on unaligned datasets. To address this issue, we propose an efficient RGB-T SOD method for real-world unaligned image pairs, termed Thin-Plate Spline-driven Semantic Correlation Learning Network (TPS-SCL). We employ a dual-stream MobileViT as the encoder, combined with efficient Mamba scanning mechanisms, to effectively model correlations between the two modalities while maintaining low parameter counts and computational overhead. To suppress interference from redundant background information during alignment, we design a Semantic Correlation Constraint Module (SCCM) to hierarchically constrain salient features. Furthermore, we introduce a Thin-Plate Spline Alignment Module (TPSAM) to mitigate spatial discrepancies between modalities. Additionally, a Cross-Modal Correlation Module (CMCM) is incorporated to fully explore and integrate inter-modal dependencies, enhancing detection performance. Extensive experiments on various datasets demonstrate that TPS-SCL attains state-of-the-art (SOTA) performance among existing lightweight SOD methods and outperforms mainstream RGB-T SOD approaches.

Existing RGB-T salient object detection methods often employ asymmetric feature processing mechanisms, which lead to limited inter-modal interaction and difficulties in achieving cross-modal semantic alignment. Moreover, due to the inherent differences in imaging mechanisms, modality conflicts caused by feature prior mismatches severely restrict the efficient exploitation of complementary information. To address these challenges, we propose a Mirror Feature-aware Generative Adversarial Network (MFAGAN). We introduce adversarial learning into the multi-modal feature fusion process, transforming the implicit feature alignment assumptions in traditional fusion methods into explicit distribution consistency constraints through the dynamic game mechanism between the generator and discriminator. Specifically, MFAGAN designs a triple collaborative optimization component: 1) A symmetric two-stage encoder achieves a dynamic balance between pixel-level details and semantic-level representations through bidirectional alternating guidance; 2) A cross-modal residual decoder employs independent parameter paths to preserve modality-specific characteristics and suppress fusion bias; 3) A feature difference complementation module adaptively integrates differential information from regions with confidence conflicts. We conduct extensive experiments on three public datasets. The experimental results show that the MFAGAN achieves better performance than the competing methods. Codes and results are released on https://github.com/asd291614761/MFAGAN.

Facial beauty analysis becomes an emerging research area due to many potential applications, such as aesthetic surgery plan, cosmetic industry, photo retouching, and entertainment. In this paper, we propose a data-driven facial beauty analysis framework that contains three application modules: prediction, retrieval, and manipulation. A beauty model is the core of the framework. With carefully designed features, the model can be built for different purposes. For prediction, we combine several low-level face representations and high-level features to form a feature vector and perform feature selection to optimize the feature set. The model built with the optimized feature set outperforms state-of-the-art methods. Then, we discuss two scenarios of beauty-oriented face retrieval: for recommendation and for beautification. Finally, we propose two approaches for facial beauty manipulation. One is an exemplar-based approach that uses the retrieved results. The other is a model-based approach that modifies facial features along the gradient of the beauty model. In this case, the model is built with the shape or appearance feature. Experimental results show that the exemplar-based approach is better for shape beautification; the model-based approach is suitable for texture beautification; and the combination of them can increase the attractiveness of a query face robustly.

In this article, a new hypothesis on facial beauty perception is proposed: the weighted average of two facial geometric features is more attractive than the inferior one between them. Extensive evidences support the new hypothesis. We collected 390 well-known beautiful face images (e.g., Miss Universe, movie stars, and super models) as well as 409 common face images from multiple sources. Dozens of volunteers rated the face images according to their attractiveness. Statistical regression models are trained on this database. Under the empirical risk principle, the hypothesis is tested on 318,801 pairs of images and receives consistently supportive results. A corollary of the hypothesis is attractive facial geometric features construct a convex set. This corollary derives a convex hull based face beautification method, which guarantees attractiveness and minimizes the before--after difference. Experimental results show its superiority to state-of-the-art geometric based face beautification methods. Moreover, the mainstream hypotheses on facial beauty perception (e.g., the averageness, symmetry, and golden ratio hypotheses) are proved to be compatible with the proposed hypothesis.