Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming

Neural enhancement through super-resolution deep neural networks opens up new possibilities for ultra-high-definition live streaming over existing encoding and networking infrastructure. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantir, the first neural-enhanced UHD live streaming system with fine-grained patch-level scheduling. In the presented solutions, two novel techniques are incorporated to make good scheduling decisions for inference overhead optimization and reduce the scheduling latency. Firstly, under the guidance of our pioneering and theoretical analysis, Palantir constructs a directed acyclic graph (DAG) for lightweight yet accurate quality estimation under any possible anchor patch set. Secondly, to further optimize the scheduling latency, Palantir improves parallelizability by refactoring the computation subprocedure of the estimation process into a sparse matrix-matrix multiplication operation. The evaluation results suggest that Palantir incurs a negligible scheduling latency accounting for less than 5.7% of the end-to-end latency requirement. When compared to the state-of-the-art real-time frame-level scheduling strategy, Palantir reduces the energy overhead of SR-integrated mobile clients by 38.1% at most (and 22.4% on average) and the monetary costs of cloud-based SR by 80.1% at most (and 38.4% on average).

Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation

Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large number of pixel masks during training is expensive. This paper considers a more challenging scenario, weakly-supervised few-shot segmentation (WS-FSS), which only provides category ($i.e.$ image-level) labels. It requires the model to learn robust support-query information when the generated mask is inaccurate. In this work, we design a Correlation Enhancement Network (CORENet) with foundation model, which utilizes multi-information guidance to learn robust correlation. Specifically, correlation-guided transformer (CGT) utilizes self-supervised ViT tokens to learn robust correlation from both local and global perspectives. From the perspective of semantic categories, the class-guided module (CGM) guides the model to locate valuable correlations through the pre-trained CLIP. Finally, the embedding-guided module (EGM) implicitly guides the model to supplement the inevitable information loss during the correlation learning by the original appearance embedding and finally generates the query mask. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ have shown that CORENet exhibits excellent performance compared to existing methods.

Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation

The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing the scalability and performance. In this paper, we propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality and diversity of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. We further enrich the hard class pseudo-labels with inter-image information through a skillfully designed hard-aware pseudo-label augmentation. Besides, we propose the region-adaptive regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. For the non-pseudo-label region, consistency constraint is also constructed to introduce stronger supervision signals during model optimization. Our method is so concise and efficient that it is easy to be generalized to other UDA methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Oxford RobotCar demonstrate the superior performance of our approach compared with the state-of-the-art methods.

Multiple Narrow-band signals Direction Finding with TMLA by Nonuniform Period Modulation

A new array signal reconstruction and signal-channel DOA estimation method based on TMLA by nonuniform period modulation are proposed. By using non-uniform period modulation, the harmonic component produced by different elements could be separated. Therefore, the conventional snapshot could be reconstructed by analyzing the spectrum of the combined signal. Then spatial spectrum estimation method is used to implement DOA estimation. Numerical simulations are provided to verify the feasibility and accuracy of the proposed method. Since the duration of the signal in the frequency domain analysis processed in a single time is very short, this method is also applicable to narrowband signals. Another highlight is that this method can simultaneously measure the number of the elements-1 angle of incident signals.