Deciphering sentiments or emotions in face-to-face human interactions is an inherent capability of human intelligence, and thus a natural goal of artificial intelligence. The proliferation of multimedia data in video sites gives rise to multimodal sentiment analysis in various applications and research fields such as movie and product review, opinion polling, and affective computing. In order to improve the performance of multimodal sentiment analysis task, this paper proposes a novel neural network with multiple stacked attention mechanism (MSAM) on multimodal data containing texts, video, and audio at an utterance level. We conduct experiments using two benchmark datasets, namely CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus, and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Compared with a comprehensive set of state-of-the-art baselines, the evaluation results demonstrate the effectiveness of our proposed MSAM network.

We propose a semantic mapping-based remote sensing image matching method, which aims to obtain the matching positions of candidate patches containing keypoints directly on the reference image, avoiding the use of cost-volume search pixel by pixel. First, a global context-fusing attention structure is created to fuse global semantic information for candidate patches with the entire sensed image. Then, a self-attention layer with semantic dependencies is proposed to extract the semantic dependencies on the reference image for cross-modal representation. The global receptive field provided by self-attention enables the proposed method to obtain the semantic mapping of candidate patches on the reference image. The experimental results show that the proposed method is insensitive to image distortion and achieves cross-modal matching of SAR-optical images with high accuracy, while still running several orders of magnitude faster. This ensures increased speed in remote sensing image analysis and pipeline processing while promoting new directions in learning-based registration.