Augmentative and Alternative Communication (AAC) technologies are categorized into two forms: aided AAC, which uses external devices like speech-generating systems to produce standardized output, and unaided AAC, which relies on body-based gestures for natural expression but requires shared understanding. We investigate how to combine these approaches to harness the speed and naturalness of unaided AAC while maintaining the intelligibility of aided AAC, a largely unexplored area for individuals with communication and motor impairments. Through 18 months of participatory design with AAC users, we identified key challenges and opportunities and developed AllyAAC, a wearable system with a wrist-worn IMU paired with a smartphone app. We evaluated AllyAAC in a field study with 14 participants and produced a dataset containing over 600,000 multimodal data points featuring atypical gestures—the first of its kind. Our findings reveal challenges in recognizing personalized, idiosyncratic gestures and demonstrate how to address them using Transformer-based large machine learning (ML) models with different pretraining strategies. In sum, we contribute design principles and a reference implementation for adaptive, personalized systems combining aided and unaided AAC.

We present IKIWISI ("I Know It When I See It"), an interactive visual pattern generator for assessing vision-language models in video object recognition when ground truth is unavailable. IKIWISI transforms model outputs into a binary heatmap where green cells indicate object presence and red cells indicate object absence. This visualization leverages humans' innate pattern recognition abilities to evaluate model reliability. IKIWISI introduces "spy objects": adversarial instances users know are absent, to discern models hallucinating on nonexistent items. The tool functions as a cognitive audit mechanism, surfacing mismatches between human and machine perception by visualizing where models diverge from human understanding. Our study with 15 participants found that users considered IKIWISI easy to use, made assessments that correlated with objective metrics when available, and reached informed conclusions by examining only a small fraction of heatmap cells. This approach not only complements traditional evaluation methods through visual assessment of model behavior with custom object sets, but also reveals opportunities for improving alignment between human perception and machine understanding in vision-language systems.

Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction. However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability and user trust. We introduce Logic-RAG, a novel Retrieval-Augmented Generation (RAG) framework that improves LMMs' spatial understanding in driving scenarios. Logic-RAG constructs a dynamic knowledge base (KB) about object-object relationships in first-order logic (FOL) using a perception module, a query-to-logic embedder, and a logical inference engine. We evaluated Logic-RAG on visual-spatial queries using both synthetic and real-world driving videos. When using popular LMMs (GPT-4V, Claude 3.5) as proxies for an autonomous driving system, these models achieved only 55% accuracy on synthetic driving scenes and under 75% on real-world driving scenes. Augmenting them with Logic-RAG increased their accuracies to over 80% and 90%, respectively. An ablation study showed that even without logical inference, the fact-based context constructed by Logic-RAG alone improved accuracy by 15%. Logic-RAG is extensible: it allows seamless replacement of individual components with improved versions and enables domain experts to compose new knowledge in both FOL and natural language. In sum, Logic-RAG addresses critical spatial reasoning deficiencies in LMMs for autonomous driving applications. Code and data are available at: https://github.com/Imran2205/LogicRAG.

This paper presents a curated list of 90 objects essential for the navigation of blind and low-vision (BLV) individuals, encompassing road, sidewalk, and indoor environments. We develop the initial list by analyzing 21 publicly available videos featuring BLV individuals navigating various settings. Then, we refine the list through feedback from a focus group study involving blind, low-vision, and sighted companions of BLV individuals. A subsequent analysis reveals that most contemporary datasets used to train recent computer vision models contain only a small subset of the objects in our proposed list. Furthermore, we provide detailed object labeling for these 90 objects across 31 video segments derived from the original 21 videos. Finally, we make the object list, the 21 videos, and object labeling in the 31 video segments publicly available. This paper aims to fill the existing gap and foster the development of more inclusive and effective navigation aids for the BLV community.

Blind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel three-wheeled, mouse-shaped stationary input device, to address this issue. Informed by participatory sessions, Wheeler enables blind users to navigate up to three hierarchical levels in an app independently using three wheels instead of navigating just one level at a time using a keyboard. The three wheels also offer versatility, allowing users to repurpose them for other tasks, such as 2D cursor manipulation. A study with 12 blind users indicates a significant reduction (40%) in navigation time compared to using a keyboard. Further, a diary study with our blind co-author highlights Wheeler’s additional benefits, such as accessing UI elements with partial metadata and facilitating mixed-ability collaboration.

This paper tackles image segmentation problems for underwater environments. First, we introduce a novel under-water animal-centric dataset with dense pixel-level annotations containing diverse fine-grained animal categories to mitigate the lack of diverse categories in the existing benchmarks. Then, we solve two image segmentation tasks using underwater images in this dataset: (i) few-shot segmentation, and (ii) semantic segmentation. For the segmentation task in a few-shot learning framework, we propose a novel attention-guided deep neural network architecture by infusing attention modules in various stages of our proposed network. We systematically explore how the learned attention maps can improve few-shot segmentation performance for underwater imagery. Finally, we assess the semantic segmentation problem on our proposed dataset by benchmarking it with two state-of-the-art semantic segmentation methods. We believe our new problem setup, i.e., few-shot segmentation for underwater environments, will be a valuable addition to the existing underwater semantic segmentation task. We believe our novel dataset will pave the way for developing better algorithms and exploring new research directions for marine robotics and underwater image understanding. We publicly release our dataset and the code to advance image understanding research in underwater environments: https://github.com/Imran220S/uwsnet.