A Fast Dejittering Approach For Line Scanning Microscopy

Visual Dialog is a typical AI-agent task on images, in which the agent interprets information from heterogeneous modalities and provides the correct answer. in this area, most approaches are based on the attention mechanism. When the agent enjoys the large-capacity advantage of attention, the lack of the right inductive bias compared with convolution hinders its process. Therefore, in order to utilize their advantages and compensate for their respective shortcomings, inspired by the paraventricular thalamus (PVT) in the brain, we couple convolution and attention, termed as Attention Convolution Enhanced (ACE) method for enhancing the agent's activation of key features and strengthening the semantic understanding of visual and textual data. Meanwhile, we propose Heuristic Adjustment (HA) module to globally strengthen the agent's semantic understanding and reduce language bias that is easy to occur after using the enhanced features. Finally, we concatenate the ACE and the HA in our Coupling Attention and Convolution for Heuristic Network (CACH-Net) to train the agent for better semantic comprehension and generalization ability. Extensive experiments on the VisDial v1.0 benchmarks show that our CACH-Net has a better performance.