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1. Spider vision

   https://doi.org/10.1016/j.cub.2020.07.042  

   Morehouse, Nathan (September 2020, Current Biology)

   null (Ed.)
2. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning

   https://doi.org/10.1145/3672556  

   Azad, Fahim Tasneema; Candan, K Selçuk; Kapkiç, Ahmet; Li, Mao-Lin; Liu, Huan; Mandal, Pratanu; Sheth, Paras; Arslan, Bilgehan; Chowell-Puente, Gerardo; Sabo, John; et al (June 2024, ACM Transactions on Spatial Algorithms and Systems)

   Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally distributed entities. In these applications, the ability to leverage spatio-temporal data to obtain causally based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this article, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in spatio-temporal data and model integration, causal learning and discovery, large scale data- and model-driven simulations, emulations, and forecasting, as well as spatio-temporal data-driven and model-centric operational recommendations, and effective causally driven visualization and explanation. We thus provide a vision, and a road map, for spatio-causal situation awareness, forecasting, and planning. 

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3. Eulerian Single-Photon Vision

   https://doi.org/10.1109/ICCV51070.2023.00960  

   Gupta, Shantanu; Gupta, Mohit (October 2023, IEEE)
4. Rethinking Vision and Action

   https://doi.org/10.1146/annurev-psych-021422-043229  

   Nakayama, Ken; Moher, Jeff; Song, Joo-Hyun (January 2023, Annual Review of Psychology)

   Action is an important arbitrator as to whether an individual or a species will survive. Yet, action has not been well integrated into the study of psychology. Action or motor behavior is a field apart. This is traditional science with its need for specialization. The sequence in a typical laboratory experiment of see → decide → act provides the rationale for broad disciplinary categorizations. With renewed interest in action itself, surprising and exciting anomalous findings at odds with this simplified caricature have emerged. They reveal a much more intimate coupling of vision and action, which we describe. In turn, this prompts us to identify and dwell on three pertinent theories deserving of greater notice. 

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5. Efficient Vision Transformer for Human Pose Estimation via Patch Selection. British Machine Vision Conference

   Kinfu, Kaleab; Vidal, Rene (September 2023, British Machine Vision Conference)

   While Convolutional Neural Networks (CNNs) have been widely successful in 2D human pose estimation, Vision Transformers (ViTs) have emerged as a promising alternative to CNNs, boosting state-of-the-art performance. However, the quadratic computational complexity of ViTs has limited their applicability for processing high-resolution images. In this paper, we propose three methods for reducing ViT’s computational complexity, which are based on selecting and processing a small number of most informative patches while disregarding others. The first two methods leverage a lightweight pose estimation network to guide the patch selection process, while the third method utilizes a set of learnable joint tokens to ensure that the selected patches contain the most important information about body joints. Experiments across six benchmarks show that our proposed methods achieve a significant reduction in computational complexity, ranging from 30% to 44%, with only a minimal drop in accuracy between 0% and 3.5%. 

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