Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features. While attention has been widely used in VL tasks, it has not been examined the capability of different attention alignment calculation in bridging the semantic gap between visual and textual clues. In this research, we conduct a comprehensive analysis on understanding the role of attention alignment by looking into the attention score calculation methods and check how it actually represents the visual region’s and textual token’s significance for the global assessment. We also analyse the conditions which attention score calculation mechanism would be more (or less) interpretable, and which may impact the model performance on three different VL tasks, including visual question answering, text-to-image generation, text-and-image matching (both sentence and image retrieval). Our analysis is the first of its kind and provides useful insights of the importance of each attention alignment score calculation when applied at the training phase of VL tasks, commonly ignored in attention-based cross modal models, and/or pretrained models. Our code is available at: https://github.com/adlnlp/Attention_VL

Domain Adaptation (DA) has brought significant progress to a wide range of computer vision tasks. However, there are few methods that explores domain adaptation on 3D point cloud data, which plays crucial role in 3D recognition tasks (i.e., autonomous driving and robotics). In a nutshell, the unique challenges of point cloud data consist of abundant spatial global and local geometric information, complicated data distribution, and more ambiguous margins. In this paper, we introduce a novel Bi-Adversarial Discrepancy Minimization network (BADM), which aligns the cross-domain point cloud data by adopting two adversarial learning units with respect to global representations. Based on PointNet, we also utilize local alignment to reduce the discrepancy of local structures. In addition, our BADM method helps define more accurate margin and decision bounds. For this 3D point cloud DA scenario, we use PointDA-10 as the benchmark, which is extracted from ModelNet, ShapeNet, and ScanNet for cross-domain 3D object classification (10 mutual classes). Extensive experiments demonstrate that our approach achieves state-of-the-art performance, outperforming other 3D DA approaches.