Visualizing restoration treatments is a crucial task in dentistry. Traditionally, dentists drag the standard template tooth line onto the inner image from the front view to simulate the outcome of the restoration. This process lacks the precision needed for patient presentation. We find that calculating the camera pose and the relative positions of the upper and lower jaws can enhance visualization accuracy and efficiency while assisting dentists in treatment design. In this work, we leverage the optical flow model and a customized point renderer to help dentists show the treatment outcome to the patient. Specifically, we take the 3D scan model and the intraoral image pair as input. Our framework automatically outputs the camera pose and the relative position of the upper and lower jaws. With these parameters, dentists can directly design the restoration treatment on the 3D scan model without caring about the 2D visualization. Then the designed tooth line and other simulation modalities can be rendered on the intraoral image with our customized renderer. Our framework relieves the labor of dentists and shows the case precisely.

Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as discontinuous pixels and inconsistent regenerated content. To address these issues, we propose a content-aware retargeting method called PruneRepaint. It incorporates semantic importance for each pixel to guide the identification of regions that need to be pruned or preserved in order to maintain key semantics. Additionally, we introduce an adaptive repainting module that selects image regions for repainting based on the distribution of pruned pixels and the proportion between foreground size and target aspect ratio, thus achieving local smoothness after pruning. By focusing on the content and structure of the foreground, our PruneRepaint approach adaptively avoids key content loss and deformation, while effectively mitigating artifacts with local repainting. We conduct experiments on the public RetargetMe benchmark and demonstrate through objective experimental results and subjective user studies that our method outperforms previous approaches in terms of preserving semantics and aesthetics, as well as better generalization across diverse aspect ratios. Codes will be available at https://github.com/fhshen2022/PruneRepaint.

Previous multi-modal transformers for RGB-D salient object detection (SOD) generally directly connect all patches from two modalities to model cross-modal correlation and perform multi-modal combination without differentiation, which can lead to confusing and inefficient fusion. Instead, we disentangle the cross-modal complementarity from two views to reduce cross-modal fusion ambiguity: 1) Context disentanglement. We argue that modeling long-range dependencies across modalities as done before is uninformative due to the severe modality gap. Differently, we propose to disentangle the cross-modal complementary contexts to intra-modal self-attention to explore global complementary understanding, and spatial-aligned inter-modal attention to capture local cross-modal correlations, respectively. 2) Representation disentanglement. Unlike previous undifferentiated combination of cross-modal representations, we find that cross-modal cues complement each other by enhancing common discriminative regions and mutually supplement modal-specific highlights. On top of this, we divide the tokens into consistent and private ones in the channel dimension to disentangle the multi-modal integration path and explicitly boost two complementary ways. By progressively propagate this strategy across layers, the proposed Disentangled Feature Pyramid module (DFP) enables informative cross-modal cross-level integration and better fusion adaptivity. Comprehensive experiments on a large variety of public datasets verify the efficacy of our context and representation disentanglement and the consistent improvement over state-of-the-art models. Additionally, our cross-modal attention hierarchy can be plug-and-play for different backbone architectures (both transformer and CNN) and downstream tasks, and experiments on a CNN-based model and RGB-D semantic segmentation verify this generalization ability.

The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs became powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserve before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networks and quantum computation and designing quantum deep neural networks (QDNNs) for acceleration on the Noisy Intermediate-Scale Quantum (NISQ) processors is also an important research problem. As one of the most widely used neural network architectures, convolutional neural network (CNN) remains to be accelerated by quantum mechanisms, with only a few attempts having been demonstrated. In this article, we propose a new hybrid quantum-classical circuit, namely, Quantum Fourier Convolutional Network (QFCN). Our model achieves exponential speedup compared with classical CNN theoretically and improves over the existing best result of quantum CNN. We demonstrate the potential of this architecture by applying it on different deep learning tasks, including traffic prediction and image classification.