The field of visual perception and analysis is rapidly advancing, with a focus on developing more accurate and robust models for image and video analysis. Recent research has highlighted the importance of incorporating cognitive and attention-based approaches to improve the performance of clinical imaging systems and object recognition models. The use of anatomy-aware and text-guided multi-modal fusion mechanisms has shown significant improvements in fine-grained segmentation tasks, such as lumbar spine segmentation. Additionally, the development of attention-guided deep learning models has enabled the effective capture of discriminative features from gait patterns for scoliosis classification. Noteworthy papers in this area include the ATM-Net framework, which employs an anatomy-aware text-guided multi-modal fusion mechanism for fine-grained lumbar spine segmentation, and the Gait-MIL method, which leverages gait patterns as biomarkers for scoliosis detection. Furthermore, research on contour integration and human-like vision has led to a better understanding of the mechanisms underlying object recognition, with implications for the development of more robust and generalizable models. Overall, these advances have the potential to significantly impact a wide range of applications, from medical imaging and diagnostics to autonomous vehicles and robotics.