Camouflaged object detection (COD) remains a significant challenge in computer vision. Existing approaches struggle to address both body immersion and structural ambiguity simultaneously, leading to inaccurate boundary delineations. This paper presents a novel Visual Sensing framework via Multiscale Edge-Aware Learning with Hybrid Attention. The proposed framework integrates hierarchical feature extraction, adaptive attention mechanisms, and progressive multi-scale fusion to achieve robust COD. We employ EfficientNetB7 as the backbone network to extract six-scale hierarchical features, capturing both fine-grained spatial details and high-level semantic representations. Initial shallow featur... More >

Video summarization (VS) aims to generate concise representations of long videos by extracting the most informative frames while maintaining essential content. Existing methods struggle to capture multi-scale dependencies and often rely on suboptimal feature representations, limiting their ability to model complex inter-frame relationships. To address these issues, we propose a multi-scale sensing network that incorporates three key innovations to improve VS. First, we introduce multi-scale dilated convolution blocks with progressively increasing dilation rates to capture temporal context at multiple levels, enabling the network to understand both local transitions and long-range dependencie... More >