Existing unsupervised keypoint detection methods apply artificial deformations to images such as masking a significant portion of images and using reconstruction of original image as a learning objective to detect keypoints. However, this approach lacks depth information in the image and often detects keypoints on the background. To address this, we propose Distill-DKP, a novel cross-modal knowledge distillation framework that leverages depth maps and RGB images for keypoint detection in a self-supervised setting. During training, Distill-DKP extracts embedding-level knowledge from a depth-based teacher model to guide an image-based student model with inference restricted to the student. Experiments show that Distill-DKP significantly outperforms previous unsupervised methods by reducing mean L2 error by 47.15% on Human3.6M, mean average error by 5.67% on Taichi, and improving keypoints accuracy by 1.3% on DeepFashion dataset. Detailed ablation studies demonstrate the sensitivity of knowledge distillation across different layers of the network. Project Page: https://23wm13.github.io/distill-dkp/

We present a whole-program optimization framework for the Taichi programming language. As an imperative language tailored for sparse and differentiable computation, Taichi's unique computational patterns lead to attractive optimization opportunities that do not present in other compiler or runtime systems. For example, to support iteration over sparse voxel grids, excessive list generation tasks are often inserted. By analyzing sparse computation programs at a higher level, our optimizer is able to remove the majority of unnecessary list generation tasks. To provide maximum programming flexibility, our optimization system conducts on-the-fly optimization of the whole computational graph consisting of Taichi kernels. The optimized Taichi kernels are then just-in-time compiled in parallel, and dispatched to parallel devices such as multithreaded CPU and massively parallel GPUs. Without any code modification on Taichi programs, our new system leads to $3.07 - 3.90\times$ fewer kernel launches and $1.73 - 2.76\times$ speed up on our benchmarks including sparse-grid physical simulation and differentiable programming.

Over the past decades, there have been significant efforts on developing robust and easy-to-use query interfaces to databases. So far, the typical query interfaces are GUI-based visual query interfaces. Visual query interfaces however, have limitations especially when they are used for accessing large and complex datasets. Therefore, we are developing a novel query interface where users can use natural language expressions to help author visual queries. Our work enhances the usability of a visual query interface by directly addressing the "knowledge gap" issue in visual query interfaces. We have applied our work in several real-world applications. Our preliminary evaluation demonstrates the effectiveness of our approach.