In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing point detection as a set prediction problem, applicable to both Manhattan and non-Manhattan world datasets. By using the positional embedding of anchor points as queries in Transformer decoders and dynamically updating them layer by layer, our method is able to directly input images and output their vanishing points without the need for explicit straight line extraction and candidate points sampling. Additionally, we introduce an orthogonal loss and a cross-prediction loss to improve accuracy on the Manhattan world datasets. Experimental results demonstrate that VPDETR achieves competitive performance compared to state-of-the-art methods, without requiring post-processing.

Panoramic audio-visual saliency detection is to segment the most attention-attractive regions in 360° panoramic videos with sound. To meticulously delineate the detected salient regions and effectively model human attention shift, we extend this task to more fine-grained instance scenarios: identifying salient object instances and inferring their saliency ranks. In this paper, we propose the first instance-level framework that can simultaneously be applied to segmentation and ranking of multiple salient objects in panoramic videos. Specifically, it consists of a distortion-aware pixel decoder to overcome panoramic distortions, a sequential audio-visual fusion module to integrate audio-visual information, and a spatio-temporal object decoder to separate individual instances and predict their saliency scores. Moreover, owing to the absence of such annotations, we create the ground-truth saliency ranks for the PAVS10K benchmark. Extensive experiments demonstrate that our model is capable of achieving state-of-the-art performance on the PAVS10K for both saliency detection and ranking tasks. The code is available at https://github.com/ruohaoguo/pavsodr.

Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process of 3D data to some extent. A recent work named xMUDA leveraged multi-modal data to domain adaptation task of 3D semantic segmentation by mimicking the predictions between 2D and 3D modalities, and outperformed the previous single modality methods only using point clouds. Based on it, in this paper, we propose a novel cross-modal contrastive learning scheme to further improve the adaptation effects. By employing constraints from the correspondences between 2D pixel features and 3D point features, our method not only facilitates interaction between the two different modalities, but also boosts feature representations in both labeled source domain and unlabeled target domain. Meanwhile, to sufficiently utilize 2D context information for domain adaptation through cross-modal learning, we introduce a neighborhood feature aggregation module to enhance pixel features. The module employs neighborhood attention to aggregate nearby pixels in the 2D image, which relieves the mismatching between the two different modalities, arising from projecting relative sparse point cloud to dense image pixels. We evaluate our method on three unsupervised domain adaptation scenarios, including country-to-country, day-to-night, and dataset-to-dataset. Experimental results show that our approach outperforms existing methods, which demonstrates the effectiveness of the proposed method.

Deep learning based 3D point cloud classification and segmentation has achieved remarkable success. Existing methods are usually implemented in the original space with 3D coordinates as inputs. However, we find that point networks taking only information of first-order coordinates hardly learn geometric features of higher order, such as point cloud normals or poses. In this study, we propose to map the input point clouds into a non-linear space to facilitate networks learning and leveraging high-order features. Firstly, we design the Parametric Veronese Mapping (PVM) function which automatically learns to map point clouds into a non-linear space. As a result, the mapped point clouds are enriched with high-order elements and maintain the basic point set properties as in the original 3D space. We can then exploit existing networks to learn high-order features from mapped point clouds. Secondly, we contribute a two-stage transformation learning module that modifies the previous one-stage module to better leverage high-order features for aligning point clouds in the projective space. Finally, an interaction module is designed to learn more discriminative features by aggregating information from both the original and projective space. Extensive experiments demonstrate that our method successfully improves the ability of most existing networks to learn high-order features and thus contributing to more accurate classification and segmentation. Moreover, the resulting models show stronger robustness to affine transformations and real-world perturbations.

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry. However, many existing approaches fail to model semantic hierarchies, which are common in the real world. We propose a new model called HRQE, which represents entities as pure quaternions. The relational embedding consists of two parts: (a) Using unit quaternions to represent the rotation part in 3D space, where the head entities are rotated by the corresponding relations through Hamilton product. (b) Using scale parameters to constrain the modulus of entities to make them have hierarchical distributions. To the best of our knowledge, HRQE is the first model that can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. Experimental results demonstrate the effectiveness of HRQE against some of the SOTA methods on four well-established knowledge graph completion benchmarks.