In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this work we propose a distributed decision-making algorithm. The agents are assumed to observe data that may be generated by different models. Through localized interactions, the agents reach agreement about which model to track and interact with each other in order to enhance the network performance. We investigate the approach for both static and mobile networks. The simulations illustrate the performance of the proposed strategies.
Bio-inspired systems are designed to mimic the behavior of some animal groups such as bee swarms, birds flying in formation, and schools of fish @cite_11 @cite_0 @cite_17 @cite_10 @cite_9 @cite_16 . Diffusion strategies can be used to model some of these coordinated types of behavior, as well as solve inference and estimation tasks in a distributed manner over networks @cite_5 @cite_1 . We may distinguish between two types of networks: single-task and multi-task networks. In single-task implementations @cite_5 @cite_1 , the networks consist of agents that are interested in the same objective and sense data that are generated by the same model. An analogy would be a school of fish tracking a food source: all elements in the fish school sense distance and direction to the same food source and are interested in approaching it. On the other hand, multi-task networks @cite_8 @cite_3 @cite_12 @cite_14 @cite_18 @cite_6 @cite_15 @cite_13 @cite_19 involve agents sensing data arising from different models and different clusters of agents may be interested in identifying separate models. A second analogy is a school of fish sensing information about multiple food sources.
@cite_13 @cite_18 @cite_11 @cite_14 @cite_8 @cite_9 @cite_1 @cite_3 @cite_6 @cite_0 @cite_19 @cite_5 @cite_15 @cite_16 @cite_10 @cite_12 @cite_17
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We develop a robust plume tracking strategy using mobile sensor networks in three dimensional (3D) fields. Inspired by the plume tracking behavior of blue crabs, we propose a stochastic model of plume spikes detected by sensing agents based on the Poisson counting process, which enables us to transform the turbulent plume field detected by sensing agents to a continuously-differential field, the minimum of which is considered as a source in the field, and shares the same location with the plume source. The transformation allows us to design and analyze source-seeking algorithms in smooth fields instead of in turbulent fields with higher fluctuation spatially and temporally. Inspired by fish schools that seek darker (shaded) regions in environments with complex lighting variations, we develop a distributed source-seeking algorithm using mobile sensor networks without explicit gradient estimation. The velocity of each agent is designed using only the measurements taken by the agent and the relative positions to its neighboring agents. We prove that, using this design, the moving direction of a three-agent group will converge to the opposite gradient direction of the field, thus, the group moves towards a source in the field. We also prove that the tracking system is input-to-state stable (ISS), indicating that the system is robust to disturbances. We then generalize the design to N-agent groups, and demonstrate the strategy in both smooth fields and turbulent fields in simulations. Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem. Our model generalizes previous works by introducing a mechanism for adding long-range attractive connections between nodes in the graph without modifying the original set of feasible solutions. This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions. To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. They combine holistic representations extracted with deep networks and body pose layout obtained with a state-of-the-art pose estimation model. We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available. We use distributed computing tools to provide a new perspective on the behavior of cooperative biological ensembles. We introduce the Ants Nearby Treasure Search (ANTS) problem, a generalization of the classical cow-path problem [10, 20, 41, 42], which is relevant for collective foraging in animal groups. In the ANTS problem, k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging, such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. We focus on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if the agents do not commence the search in synchrony, then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is Ω(D + D2 k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation. We present a tight bound for the competitive penalty that must be paid, in the running time, if the agents have no information about k. Specifically, this bound is slightly more than logarithmic in the number of agents. In addition, we give a lower bound for the setting in which the agents are given some estimation of k. Informally, our results imply that the agents can potentially perform well without any knowledge of their total number k, however, to further improve, they must use some information regarding k. Finally, we propose a uniform algorithm that is both efficient and extremely simple, suggesting its relevance for actual biological scenarios. Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.