OccluNet, a spatio-temporal attention-based detector, is claimed to outperform frame-based YOLOv11 baselines for occlusion detection in DSA sequences.
Tactile Vega-Lite: Rapidly Prototyping Tactile Charts with Smart Defaults
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Tactile charts are essential for conveying data to blind and low vision (BLV) readers but are difficult for designers to construct. Non-expert designers face barriers to entry due to complex guidelines, while experts struggle with fragmented and time-consuming workflows that involve extensive customization. Inspired by formative interviews with expert tactile graphics designers, we created Tactile Vega-Lite (TVL): an extension of Vega-Lite that offers tactile-specific abstractions and synthesizes existing guidelines into a series of smart defaults. Predefined stylistic choices enable non-experts to produce guideline-compliant tactile charts quickly. Expert users can override defaults to tailor customizations for their intended audience. In a user study with 12 tactile graphics creators, we show that Tactile Vega-Lite enhances flexibility and consistency by automating tasks like adjusting spacing and translating braille while accelerating iterations through pre-defined textures and line styles. Through expert critique, we also learn more about tactile chart design best practices and design decisions.
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OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA
OccluNet, a spatio-temporal attention-based detector, is claimed to outperform frame-based YOLOv11 baselines for occlusion detection in DSA sequences.