Segment Anything Model 2 (SAM2) has demonstrated state-of-the-art performance in image/video object segmentation across many domains, but its large encoder makes it challenging for resource-constrained devices or real-time applications. One solution to this problem is to carry out knowledge distillation from the bulky encoder to a lightweight encoder, but this can result in degraded performance. In this work, we investigate multi-teacher distillation to mitigate performance degradation for distilled segmentation models. Using several foundation teacher models, our multi-teacher distilled models achieve 3.2 times speedup during end-to-end inference compared to SAM2 while achieving the best results of 74.4 and 71.1 (72.1 and 69.6 for single-teacher distillation) mIoU on the COCO and LVIS image segmentation datasets, as well as showing competitive results on video segmentation. Our results show that multi-teacher distillation offers a powerful solution for efficient image/video segmentation, while also maintaining compelling performance.

Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications. However, people's perception and understanding are often distorted by their cognitive biases, such as confirmation bias, anchoring bias, availability bias, to name a few. In this work, we use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting, and mitigate their negative effects on collaborative performance. To this end, we mathematically model cognitive biases and provide a general framework through which researchers and practitioners can understand the interplay between cognitive biases and human-AI accuracy. We then focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration. We implement a time-based de-anchoring strategy and conduct our first user experiment that validates its effectiveness in human-AI collaborative decision-making. With this result, we design a time allocation strategy for a resource-constrained setting that achieves optimal human-AI collaboration under some assumptions. We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.

Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classification output score, which can be displayed as a saliency map that highlights important pixels. Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i.e. their “fidelity”). We therefore investigate existing metrics for evaluating the fidelity of saliency methods (i.e. saliency metrics). We find that there is little consistency in the literature in how such metrics are calculated, and show that such inconsistencies can have a significant effect on the measured fidelity. Further, we apply measures of reliability developed in the psychometric testing literature to assess the consistency of saliency metrics when applied to individual saliency maps. Our results show that saliency metrics can be statistically unreliable and inconsistent, indicating that comparative rankings between saliency methods generated using such metrics can be untrustworthy.

As neural networks gain importance with several successful applications of them, this paper raises the question of how they can be applied in the context of coalition operations. A key challenge in military coalition operations is that of energy and severe bandwidth constraints. We address this challenge by exploring the use of Deep Neural Networks (DNNs) and splitting them across multiple edge nodes. Further, we explore the idea of using spiking neural networks that can lower the energy consumption significantly. Preliminary results show that both these approaches can have significant impact on coalition operations.

Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this demonstration we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex time-critical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the time-critical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. There are a large number of workflow systems designed to work in various scientific domains, including support for the Internet of Things (IoT). One such workflow system is Node-RED, which is designed to bring workflow-based programming to IoT. The main focus of this demonstration is to show how we can migrate Node-RED workflows into a decentralized execution environment, so that such workflows can run on Edge networks.

Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed because military decision-makers must be able to justify their decisions. Adversarial attacks arise because many ML algorithms are very sensitive to certain kinds of input perturbations. In this paper, we argue that these two issues are conceptually linked, and insights in one can provide insights in the other. We illustrate these ideas with relevant examples from the literature and our own experiments.