Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics. While research in model-based 6D object pose estimation has delivered promising results, model-free methods are hindered by the high computational load in rendering and inferring consistent poses of arbitrary objects in a live RGB-D video stream. To address this issue, we present 6DOPE-GS, a novel method for online 6D object pose estimation \& tracking with a single RGB-D camera by effectively leveraging advances in Gaussian Splatting. Thanks to the fast differentiable rendering capabilities of Gaussian Splatting, 6DOPE-GS can simultaneously optimize for 6D object poses and 3D object reconstruction. To achieve the necessary efficiency and accuracy for live tracking, our method uses incremental 2D Gaussian Splatting with an intelligent dynamic keyframe selection procedure to achieve high spatial object coverage and prevent erroneous pose updates. We also propose an opacity statistic-based pruning mechanism for adaptive Gaussian density control, to ensure training stability and efficiency. We evaluate our method on the HO3D and YCBInEOAT datasets and show that 6DOPE-GS matches the performance of state-of-the-art baselines for model-free simultaneous 6D pose tracking and reconstruction while providing a 5$\times$ speedup. We also demonstrate the method's suitability for live, dynamic object tracking and reconstruction in a real-world setting.

This paper proposes an extension of graph patterns, referred to as ontological graph patterns (OGPs), to accelerate ontology-mediated query answering. OGPs employ graph patterns to support topological queries, attach conditions to both vertices and edges to specify additional restrictions, and support conditional partial matching semantics. Hence, OG Ps can express conjunctive queries (CQs) under ontological constraints. We develop a PTIME algorithm to generate an equivalent OGP from a CQ over the ontology specified by description logic$DL-Lite_{\mathcal{R}}$, and design a matching algorithm to match OGPs in graphs. Using real-life and synthetic data, we experimentally verify that the proposed approach outperforms the state-of-the-art algorithms for ontology-mediated query answering by 2–3 orders of magnitude.

Speaker recognition achieved great progress recently, however, it is not easy or efficient to further improve its performance via traditional solutions: collecting more data and designing new neural networks. Aiming at the fundamental challenge of speech data, i.e. low information density, multimodal learning can mitigate this challenge by introducing richer and more discriminative information as input for identity recognition. Specifically, since the face image is more discriminative than the speech for identity recognition, we conduct multimodal learning by introducing a face recognition model (teacher) to transfer discriminative knowledge to a speaker recognition model (student) during training. However, this knowledge transfer via distillation is not trivial because the big domain gap between face and speech can easily lead to overfitting. In this work, we introduce a multimodal learning framework, VGSR (Vision-Guided Speaker Recognition). Specifically, we propose a MKD (Margin-based Knowledge Distillation) strategy for cross-modality distillation by introducing a loose constrain to align the teacher and student, greatly reducing overfitting. Our MKD strategy can easily adapt to various existing knowledge distillation methods. In addition, we propose a QAW (Quality-based Adaptive Weights) module to weight input samples via quantified data quality, leading to a robust model training. Experimental results on the VoxCeleb1 and CN-Celeb datasets show our proposed strategies can effectively improve the accuracy of speaker recognition by a margin of 10% ∼ 15%, and our methods are very robust to different noises.

Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners.

Through silicon vias (TSVs) play an important role as the vertical electrical connections in 3-D stacked integrated circuits. However, the closely clustered TSVs suffer from the crosstalk noise between the neighboring TSVs, and result in the extra delay and the deterioration of signal integrity. For a 3 × 3 TSV array, the severity of crosstalk noise in the center victim TSV is classified into 11 levels, which is defined as 0C to 10C from low noise to high noise, depending on the combinations of the digital patterns applied to the TSV array. An enhanced code based on the Fibonacci number system (FNS) to suppress the crosstalk noise below 6C level is proposed, in which both the redundancy of numbers and the nonuniqueness of Fibonacci-based binary codeword are utilized to search the proper codeword. Experimental results show that the proposed technique decreases about 22% latency of TSVs comparing with the worst crosstalk cases. This technique is applicable in the large-scale TSV array for it has a quasi-linear hardware overhead, and its system overhead is less than that of the 3-D 4-LAT counterpart if the data width is greater than 18, and it has good usability for it consumes less power per TSV and achieves lower bit error rate at the interested frequency range comparing with that of the original FNS coding technique.

With the development of 3D integration technology, microsystems with vertical interconnects are attracting attention from researchers and industry applications. Basic elements of integrated passive devices (IPDs), including inductors, capacitors, and resistors, could dramatically save the footprint of the system, optimize the form factor, and improve the performance of radio frequency (RF) systems. In this paper, IPDs using thin film built-up technology are introduced, and the design and characterization of coplanar waveguides (CPWs), inductors, and capacitors are presented.