Liquid crystal optics are highly favorable for AR/VR/MR near‐eye displays because of the capability of modulating polarization and phase in an ultra‐compact profile. In this presentation, we discuss state‐of‐the‐art LC polarization hologram solutions for AR/VR/MR display technical needs, followed by remaining challenges and opportunities in the way towards high‐image‐quality and all‐day‐wearable near‐eye displays.
Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a utility function, which theoretically satisfies the Nash Equilibrium, is defined to measure the benefit/loss of players during graph evolution. Furthermore, a collaboration and competition mechanism is introduced to increase the discriminant learning ability. Under this graph game embedding framework, considering different interaction manners of nodes, we propose two specific models, named paired game embedding for paired nodes and group game embedding for group interaction. Comparing with existing graph embedding methods, our algorithm possesses two advantages: (1) the designed utility function ensures the stable graph evolution with theoretical convergence and Nash Equilibrium satisfaction; (2) the introduced collaboration and competition mechanism endows the graph game embedding framework with discriminative feature leaning ability by guiding each node to learn an optimal strategy distinguished from others. We test the proposed method on three public datasets about citation networks, and the experimental results verify the effectiveness of our method.
We propose a new resonant excitation method via the evanescent field surrounding optical nanofiber. Based on this method, we successfully obtain the fluorescence blinking signal of single colloidal quantum dot in room temperature.
Graphs play an important role in cross-modal image-text understanding as they characterize the intrinsic structure which is robust and crucial for the measurement of crossmodal similarity. In this work, we propose a Wasserstein Coupled Graph Learning (WCGL) method to deal with the cross-modal retrieval task. First, graphs are constructed according to two input cross-modal samples separately, and passed through the corresponding graph encoders to extract robust features. Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning. Based on this dictionary, the input graphs can be transformed into the dictionary space to facilitate the similarity measurement through a Wasserstein Graph Embedding (WGE) process. The WGE could capture the graph correlation between the input and each corresponding key through optimal transport, and hence well characterize the inter-graph structural relationship. To further achieve discriminant graph learning, we specifically define a Wasserstein discriminant loss on the coupled graph keys to make the intra-class (counterpart) keys more compact and inter-class (non-counterpart) keys more dispersed, which further promotes the final cross-modal retrieval task. Experimental results demonstrate the effectiveness and state-of-the-art performance.
Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are usually unavailable in real scenarios. Moreover, these methods are typically designed for a specific demographic group and are not general enough. In this paper, we propose a false positive rate penalty loss, which mitigates face recognition bias by increasing the consistency of instance False Positive Rate (FPR). Specifically, we first define the instance FPR as the ratio between the number of the non-target similarities above a unified threshold and the total number of the non-target similarities. The unified threshold is estimated for a given total FPR. Then, an additional penalty term, which is in proportion to the ratio of instance FPR overall FPR, is introduced into the denominator of the softmax-based loss. The larger the instance FPR, the larger the penalty. By such unequal penalties, the instance FPRs are supposed to be consistent. Compared with the previous debiasing methods, our method requires no demographic annotations. Thus, it can mitigate the bias among demographic groups divided by various attributes, and these attributes are not needed to be previously predefined during training. Extensive experimental results on popular benchmarks demonstrate the superiority of our method over state-of-the-art competitors. Code and trained models are available at https://github.com/Tencent/TFace.
In this paper, we review liquid-crystal-on-silicon (LCoS) technology and focus on its new application in emerging augmented reality (AR) displays. In the first part, the LCoS working principles of three commonly adopted LC modes—vertical alignment and twist nematic for amplitude modulation, and homogeneous alignment for phase modulation—are introduced and their pros and cons evaluated. In the second part, the fringing field effect is analyzed, and a novel pretilt angle patterning method for suppressing the effect is presented. Moreover, we illustrate how to integrate the LCoS panel in an AR display system. Both currently available intensity modulators and under-developing holographic displays are covered, with special emphases on achieving high image quality, such as a fast response time and high-resolution. The rapidly increasing application of LCoS in AR head-mounted displays and head-up displays is foreseeable.
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.
A 4,000 PPI phase‐only NCTU PCU‐3‐01 LCoS‐SLM filled with UCF‐L1 NLC was assembled and evaluated. The resulting LCoS panel possessed 1.6 ms of linear‐full‐phase‐modulation response time under digital driving (V w = 4.5 V, V b = 0.3 V) at T = 45 °C. The 3D holographic images show that most diffracted light is transferred within 2 ms. Meanwhile, a low‐latency driver solution based on 1920 x 640 at 240 Hz (equivalent to 4000 PPI) input frame rate and 720 Hz data frame rate were presented.
An optimized blue‐phase liquid crystal (BPLC) with a moderate dielectric constant, manageable capacitor charging time, high voltage holding ratio, and submillisecond gray‐to‐gray response time at room temperature is developed. Using protruded triangular IPS electrodes, the operation voltage can be reduced to 15V while keeping ~74% transmittance. Such a BPLC enables single‐TFT addressing and 240‐Hz operation. Potential application for color‐sequential displays is emphasized. Our new approaches have solved the most critical charging time issue and would accelerate the emergence of the long‐awaited blue‐phase LCDs. The primetime for BPLCD is around the corner.
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images’ visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.
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