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In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance. This sparse, vectorized boundary representation for objects, while attractive in many downstream computer vision tasks, quickly runs into issues of parity that need to be addressed: parity in supervision and parity in performance when compared to existing pixel-based methods. This is due in part to object instances being annotated with ground-truth in the form of polygonal boundaries or segmentation masks, yet being evaluated in a convenient manner using only segmentation masks. Our method, BoundaryFormer, is a Transformer based architecture that directly predicts polygons yet uses instance mask segmentations as the ground-truth supervision for computing the loss. We achieve this by developing an end-to-end differentiable model that solely relies on supervision within the mask space through differentiable rasterization. BoundaryFormer matches or surpasses the Mask R-CNN method in terms of instance segmentation quality on both COCO and Cityscapes while exhibiting significantly better transferability across datasets. 

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signals to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised strategy and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed. 

In this paper, we consider the problem of joint offloading and wireless scheduling design for parallel computing applications with hard deadlines. This is motivated by the rapid growth of compute-intensive mobile parallel computing applications (e.g., real-time video analysis, language translation) that require to be processed within a hard deadline. While there are many works on joint computing and communication algorithm design, most of them focused on the minimization of average computing time and may not be applicable for mobile applications with hard deadlines. In this work, we explicitly take hard deadlines for computing tasks into account and develop a joint offloading and scheduling algorithm based on the stochastic network optimization framework. The proposed algorithm is shown to achieve average energy consumption arbitrarily close to the optimal one. However, this algorithm involves a strong coupling between offloading and scheduling decisions, which yields significant challenges on its implementation. Towards this end, we first successfully decouple the offloading and scheduling decisions in the case with one time slot deadline by exploring the intrinsic structure of the proposed algorithm. Based on this, we further implement the proposed algorithm in the general setups. Simulations are provided to corroborate our findings.