Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M–1.32 M) and the number of floating-point operations (0.59 G–0.6 G) when compared to recent state-of-the-art methods.

Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing.

The Sharpness-Aware Minimization (SAM) optimizer connects flatness and generalization, suggesting that loss basins with lower sharpness are correlated with better generalization. However, SAM requires manually tuning the open ball radius, which complicates its practical application. To address this, we propose a method inspired by linear connectivity, using two models initialized differently as endpoints to automatically determine the optimal open ball radius. Specifically, we introduce distance regularization between the two models during training, which encourages them to approach each other, thus dynamically adjusting the open ball radius. We design an optimization algorithm called ’Twin Stars Entwined’ (TSE), where the stopping condition is defined by the models’ linear connectivity, i.e., when they converge to a region of sufficiently low distance. As the models iteratively reduce their distance, they converge to a flatter region of the loss landscape. Our approach complements SAM by dynamically identifying flatter regions and exploring the geometric properties of multiple connected loss basins. Instead of searching for a single large-radius basin, we identify a group of connected basins as potential optimization targets. Experiments conducted across multiple models and in varied noise environments showed that our method achieved a performance on par with state-of-the-art techniques.

This paper proposes an Online Prototype Angular Balanced Self-Distillation (OPAB) framework to address the challenges posed by non-ideal annotation in remote sensing image semantic segmentation. “Non-ideal annotation” typically refers to scenarios where long-tailed class distributions and label noise coexist in both training and testing sets. Existing methods often tackle these two issues separately, overlooking the conflict between noisy samples and minority classes as well as the unreliable early stopping caused by non-clean validation sets, which exacerbates the model’s tendency to memorize noisy samples. OPAB mitigates the imbalance problem by employing an improved bilateral-branch network (BBN) that integrates max-min angular regularization (MMA) and category-level inverse weighting to achieve balanced hyperspherical representations. The balanced hyperspherical representations further facilitate noise-clean sample separation and early stopping estimation based on large category-wise Local Intrinsic Dimensionality (LID). Moreover, OPAB introduces a bootstrap teacher label refinement strategy coupled with a student full-parameter retraining mechanism to avoid memorizing noisy samples. Experimental results on ISPRS datasets demonstrate that OPAB achieves a 2.0% mIoU improvement under non-ideal annotation conditions and achieves 89% mIoU after cross-set correction, showcasing strong robustness across different backbones and effective iterative calibration capability.