Visual AI
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Visual AI tracks nearly 100 wildlife species to improve conservation
Visual AI tracks nearly 100 wildlife species to improve conservation Gaby Clark Scientific Editor Robert Egan Associate Editor Wildlife research projects worldwide could benefit from a new AI system which can automatically find, name, and follow individual animals in footage. A University of Bristol team working on Animal Biometrics and AI for Conservation have been key contributors to the SA-FARI (Segment Anything in Footage of Animals for Recognition and Identification) project, developed...
Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
Announce Type: new Abstract: Conversational AI has enabled users to generate and interpret visualizations through natural language, significantly lowering the technical barrier to entry. The increased accessibility brings visualization novices into data visualization, but also exposes them to misinformation and misinterpretations. We are motivated to examine what issues can arise in interactions with current conversational AI, whether visualization novices can recognize such issues, and how...
SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation
arXiv:2606.03348v1 Announce Type: new Abstract: Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that...
Cohort-based Semantic Labeling: AI-Enabled Recovery of Visualization Semantics from Deployed SVGs
arXiv:2606.09782v1 Announce Type: new Abstract: Many web-based visualizations are deployed as Scalable Vector Graphics (SVG), a format that faithfully preserves visual appearance but typically omits the higher-level semantic structure needed for machine interpretation. Once rendered and published, information about a visualization's components, roles, and encodings is no longer explicitly available, limiting downstream operations such as querying, accessibility augmentation, explanation,...
AIDEN: Design and Pilot Study of an AI Assistant for the Visually Impaired
arXiv:2511.06080v4 Announce Type: replace Abstract: This paper presents AIDEN, an artificial intelligence-based assistant designed to enhance the autonomy and daily quality of life of visually impaired individuals, who often struggle with object identification, text reading, and navigation in unfamiliar environments. Existing solutions such as screen readers or audio-based assistants facilitate access to information but frequently lead to auditory overload and raise privacy concerns in open...
On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection
new Abstract: Visual monitoring systems that rely on cloud-based AI inference expose raw image data to external services, creating fundamental tensions with the data-minimisation principle of the General Data Protection Regulation (GDPR). This paper presents a proof-of-concept privacy-by-design pipeline that resolves this tension by confining all inference entirely to the edge device. A YOLOv5n-seg model compiled for a Hailo-8L AI accelerator delivers real-time object detection on a...
AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study
new Abstract: Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies.
AI from concrete to abstract: demystifying artificial intelligence to the general public
arXiv:2006.04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSARD weightless artificial neural networks, this article presents a new methodology, AI from concrete to abstract (AIcon2abs), to enable general people (including children) to achieve this goal.
A Visually Impaired Assistance Benchmark for VLM-as-a-Judge Evaluation
arXiv:2605.31351v1 Announce Type: new Abstract: AI-based Visually Impaired Assistance (VIA) remains challenging, largely due to the high cost of human evaluation. The VLM-as-a-Judge paradigm may offer a promising alternative, although it has mostly been studied in general domains. We therefore ask whether such judges can be trusted for VIA tasks.
LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
Announce Type: new Abstract: Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems require the joint optimization of highly coupled parameters, yet traditional hyperparameter optimization (HPO) methods -- including Tree-structured Parzen Estimators (TPE) and Gaussian Process Bayesian Optimization -- rely on an independence assumption that fundamentally prevents them from...