In complex visual scenarios, people can understand occluded objects through contextual reasoning and association. However, existing video object detection (VOD) methods could not work well. They treat all frames equally during feature aggregation and ignore the interdependence of objects between adjacent frames. This results in excessive noise during long-term feature alignment. To address these issues, we propose a task-specific spatiotemporal context-aware decoupling (TSCD) network, which adapts the global semantic information and local localization information needed for classification and regression tasks, respectively. Furthermore, a spatiotemporal context-aware feature matching (CAFM) module is utilized to refine the regression features while avoiding inter-frame feature mixing. Effective object associations are used to improve the regression of occluded object locations. Extensive experimental results on the ImageNet VID dataset and the OVIS dataset show that our proposed TSCD has significant advantages in occlusion scenes. The code is available at https://github.com/KaihongLi/TSCD.

Lightweight super-resolution (SR) has garnered attention for balancing performance and efficiency in resource-constrained environments. In lightweight SR tasks, traditional CNN-based methods are constrained by limited receptive fields, leading to suboptimal SR performance. In contrast, ViT-based models achieve remarkable results but suffer from significant computational burden due to the self-attention mechanism. In this paper, we propose adaptive Pixel Classification and equivalent Large Kernels Network (PCLKN), a novel lightweight SR model that addresses the limitations of traditional CNN-based and ViT-based methods. PCLKN utilizes equivalent large kernels to expand the receptive field while relying solely on convolutional operations, significantly reducing computational overhead. Additionally, it integrates global priors with spatial and channel attention to enhance feature extraction and leverages adaptive pixel classification to utilize similar pixel information for reconstruction. Experimental results on benchmark datasets demonstrate that PCLKN achieves superior SR performance with an excellent trade-off between performance and computational complexity.

In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation. Experiments demonstrate that our model can be applied to dataset of arbitrary prior shapes, and can cope with image noise and clutter, as well as partial occlusions.