Modeling complex rigid motion across large spatiotemporal spans remains an unresolved challenge in dynamic reconstruction. Existing paradigms are mainly confined to short-term, small-scale deformation and offer limited consideration for physical consistency. This study proposes PMGS, focusing on reconstructing Projectile Motion via 3D Gaussian Splatting. The workflow comprises two stages: 1) Target Modeling: achieving object-centralized reconstruction through dynamic scene decomposition and an improved point density control; 2) Motion Recovery: restoring full motion sequences by learning per-frame SE(3) poses. We introduce an acceleration consistency constraint to bridge Newtonian mechanics and pose estimation, and design a dynamic simulated annealing strategy that adaptively schedules learning rates based on motion states. Futhermore, we devise a Kalman fusion scheme to optimize error accumulation from multi-source observations to mitigate disturbances. Experiments show PMGS’s superior performance in reconstructing high-speed nonlinear rigid motion compared to mainstream dynamic methods.

Continual Learning with Pre-trained Models holds great promise for efficient adaptation across sequential tasks. However, most existing approaches freeze PTMs and rely on auxiliary modules like prompts or adapters, limiting model plasticity and leading to suboptimal generalization when facing significant distribution shifts. While full fine-tuning can improve adaptability, it risks disrupting crucial pre-trained knowledge. In this paper, we propose Mutual Information-guided Sparse Tuning (MIST), a plug-and-play method that selectively updates a small subset of PTM parameters, less than 5%, based on sensitivity to mutual information objectives. MIST enables effective task-specific adaptation while preserving generalization. To further reduce interference, we introduce strong sparsity regularization by randomly dropping gradients during tuning, resulting in fewer than 0.5% of parameters being updated per step. Applied before standard freeze-based methods, MIST consistently boosts performance across diverse continual learning benchmarks. Experiments show that integrating our method into multiple baselines yields significant performance gains.

Tracking multiple objects in videos captured by unmanned aerial vehicles is challenging due to sudden viewpoint changes, non-linear motion trajectories, and rapid variations in target size and appearance. Existing methods often struggle to handle these complexities, as they rely heavily on handcrafted geometric constraints and fail to adapt to significant field-of-view changes. To address these issues, this paper presents the Dynamic Field-Aware Multi-Object Tracker (DFA-MOT), a joint detection and tracking framework that integrates detection and motion prediction into a unified model, enhancing tracking performance in dynamic UAV environments. The proposed Dynamic Field-of-View Consistency Learning (DFCL) module mitigates geometric distortions caused by UAV movement by leveraging optical flow and learnable deformation operations to achieve progressive spatial alignment. A Scale-Aware Tracking (SAT) mechanism is explored, which enables to accurately predict of both position and scale variations, enhancing the model’s adaptability to variations in target size. By combining detection with predictive motion modeling, DFA-MOT effectively overcomes the limitations of traditional manual constraints. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that DFA-MOT significantly outperforms state-of-the-art tracking methods in UAV scenarios.

Object detection has significantly progressed with Detection Transformer (DETR) models, which achieve high accuracy through Transformer architectures. However, DETR’s substantial computational demands limit its suitability for real-time applications on resource-constrained devices, where lightweight Convolutional Neural Networks (CNNs) are preferred. While knowledge distillation (KD) is an appealing technique to compress giant detectors into smaller ones, existing KD methods struggle to efficiently transfer knowledge from DETRs to CNN-based detectors due to a substantial semantic gap. To address this, we propose the DETR-CNN Knowledge Distillation (DCKD) framework. Our framework decomposes the knowledge in DETR into Homogeneous and Heterogeneous categories and introduces a novel approach integrating Heterogeneous Knowledge Logits Distillation (HeKLD) and Homogeneous Knowledge Feature Distillation (HoKFD) to bridge the semantic gap between DETR and CNN-based detectors. HeKLD aligns cross-head prediction outputs to bridge architectural differences and ensure consistent decision-making, while HoKFD transfers low-level features by focusing on relevant regions to enhance detection accuracy. Extensive experiments on the COCO dataset demonstrate that our DCKD significantly improves mean Average Precision (mAP) across various teacher-student pairs, consistently outperforming the latest state-of-the-art KD methods.

Convolutional neural networks(CNNs) currently dominate the field of computer vision, where the pooling layer plays an important role in reducing computational effort and avoiding overfitting. However, the commonly used methods do not design the pooling layer from the perspective of frequency. In this paper, we propose a Lifting Scheme-based frequency-dividing downsampling framework and describe a pooling layer called frequency-dividing pooling (FDP). The two branches of the Lifting Scheme process the images by frequency, which not only enhances the interpretability of the neural network but also improves the classification accuracy of the neural network. We conduct experiments on three standard datasets and the results all demonstrate that our proposed FDP is effective.

Although there are a variety of approaches to solve multiobjective optimization problems, few of them makes systematic use of the neighborhood relationship between the candidate solutions observed during the search process to improve the final results. In this paper, a new evolutionary algorithm, referred to as the neighborhood knowledge-based evolutionary algorithm (NKEA), is proposed to solve the multiobjective optimization problem. NKEA not only takes into account the advantages of NSGA-II, and JGGA, such as the fast nondominated sorting algorithm and horizontal transmission of information in a candidate solution, but also exploits systematically the neighborhood knowledge acquired during the search process. Specifically, NKEA consists of three major stages: the direction learning stage, the mutual adaptation stage, and the self adaptation stage. NKEA not only uses the fast nondominated sorting algorithm to find the Pareto optimal solutions, but also adopts the elitist strategy to maintain the best individuals for the next generation based on this strategy. Two adaptive control functions in the mutual adaptation stage and the self adaptation stage are designed to adjust the respective contributions of coarse local search and fine local search, which allows NKEA to perform a more thorough local search. Finally, we introduce a new notion, known as the measure space, which integrates multiple measures, such as the convergence metric and the diversity metric, to evaluate the performance of the algorithm. The results of our experiments show that NKEA not only achieves good performance in a large number of multiobjective optimization problems, but also outperforms most of the state-of-the-art approaches in these problems.

In this paper, we propose a new evolutionary algorithm called nearest neighbor evolutionary algorithm (NNE) to solve the unconstrained optimization problem. Specifically, NNE consists of two major steps: coarse nearest neighbor evolutionary and fine nearest neighbor evolutionary. The coarse nearest neighbor evolutionary step pays more attention to searching the optimal solutions in the global way, while the fine nearest neighbor evolutionary step focuses on searching the best solutions in the local way. NNE repeats two major steps until the terminate condition is reached. NNE not only adopts the elitist strategy and maintains the best individuals for the next generation, but also considers the knowledge obtained in the searching process. The experiments demonstrate that (1) NNE achieves good performance in most of numerical optimization problems; (2) NNE outperforms most of state-of-art evolutionary algorithms, such as traditional genetic algorithm (GA), the jumping gene genetic algorithm (JGGA).

Although there exist a lot of approaches to solve constrained optimization problem, few of them makes use of the knowledge obtained in the searching process. In the paper, a new algorithm called nearest neighbor evolutionary algorithm (NNE) is proposed to solve the constrained optimization problem. NNE not only performs global search and local search in the searching process, but also considers the knowledge obtained in the searching process. NNE also avail itself of the elitist strategy and keeps the best individuals for the next generation. The results in the experiments show that NNE not only achieves good performance in a lot of constrained optimization problems, but also outperforms most of state-of-art approaches in most of constrained optimization problems, such as ASCHEA and SEMS.