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Brain-inspired approaches can efficiently analyze the activities of biological neural networks and solve computationally hard problems with energy efficiencies unattainable with von Neumann architectures, indicating a significant improvement in the understanding of neuronal communications and functionalities. Here, we present a brain-inspired multimodal signal processing system with organic memristor arrays that can potentially integrate the signal sensory, storage, and computation. To facilitate the multimodal signal processing system design, we used four components. First, we present a multimodal signal sensory module mainly responsible for multimodal (iconic, echoic, olfactory, muscular, and gustatory) signal collection, fusion, and storage. Second, a high-density cross-point memristive synapse array is constructed after fabrication of the albumin protein memristor to realize the dense connectivity between layers of computing, data storage, and communication. Third, considering the structure and function of the brain region, we demonstrate a general learning module for hierarchy learning, which can recognize and imagine multimodal information. Finally, the necessary peripheral circuit module (consisting of winnerless competition function circuit, analog-to-digital converter, digital-to-analog converter, pulse modulator, etc.) is designed. Notably, our system can capture massive amounts of data every second and perform in situ processing of multimodal signals. This study is expected to help achieve the deep integration of nano materials into neuromorphic computing systems and energy-efficient integrated circuits. [ABSTRACT FROM AUTHOR]

With the widespread use of deep learning in single object tracking task, mainstream tracking algorithms treat tracking as a combined classification and regression problem. Classification aims at locating an arbitrary target, and regression aims at estimating the corresponding bounding box. In this paper, we focus on regression and propose a novel box estimation network, which consists of a transformer encoder target pyramid guide (TPG) and transformer decoder target pyramid spread (TPS). Specifically, the transformer encoder TPG is designed to generate fine-grained prior knowledge with explicit representation for template targets. In contrast to the raw transformer encoder, we capture the visual dependence through local-global self-attention and deem the multi-scale target regions as the “local” region. Using this fine-grained prior knowledge, we design the transformer decoder TPS to spread it to the subsequent search regions with high affinity to accurately estimate the bounding boxes. Considering that self-attention fails to model information interaction across channels between the template target and search regions, we develop a channel-wise cross-attention block within the TPS as compensation. Extensive experiments on the OTB100, UAV123, NFS, VOT2020, VOT2021, LaSOT, LaSOT_ext, TrackingNet and GOT-10k benchmarks show that the proposed box estimation network outperforms most existing box estimation methods. Furthermore, our trackers based on this estimation network exhibit a competitive performance against state-of-the-art trackers. [ABSTRACT FROM AUTHOR]