Computer Vision

When you tell an AI, “make the third bar blue,” what does it actually see? If it’s a typical large language model (LLM), it doesn’t really see anything. It parses your instruction, guesses what “third bar” means, and fumbles to write chart code—often missing the mark. ChartM$^3$ (Multimodal, Multi-level, Multi-perspective) changes the game. It challenges AIs to not only read and write code but also visually comprehend what a user points at. With 1,000 human-curated chart editing tasks and 24,000 training examples, this new benchmark sets a higher bar—one that demands both verbal and visual fluency. ...

Open-vocabulary object detection — the holy grail of AI systems that can recognize anything in the wild — has been plagued by fragmented training strategies. Models like OWL-ViT and Grounding DINO stitch together multiple learning objectives across different stages. This Frankensteinian complexity not only slows progress, but also creates systems that are brittle, compute-hungry, and hard to scale. Enter OmniTrain: a refreshingly elegant, end-to-end training recipe that unifies detection, grounding, and image-text alignment into a single pass. No pretraining-finetuning sandwich. No separate heads. Just a streamlined pipeline that can scale to hundreds of thousands of concepts — and outperform specialized systems while doing so. ...

In an age where aging infrastructure meets accelerating AI, a new paper out of George Mason University proposes a novel question: Can large language models interpret what even seasoned engineers find difficult — NDE contour maps of bridges? The answer, based on this pilot study, is a cautious but resounding yes — with caveats that echo through the entire field of AI-assisted engineering. The Problem: Data Is There — Expertise Isn’t Always Bridges are scanned using advanced non-destructive evaluation (NDE) tools — Ground Penetrating Radar (GPR), Electrical Resistivity (ER), Impact Echo (IE), and Ultrasonic Surface Waves (USW) — but interpreting those outputs requires human expertise, which is not always available, especially during emergency assessments or in rural areas. Contour maps from these tools don’t speak for themselves. ...

The latest advancement in fake news detection doesn’t just analyze what is said—it also looks at how it feels. The SEER model (Semantic Enhancement and Emotional Reasoning Network) introduces an innovative approach that harnesses emotional reasoning and semantic depth to surpass existing benchmarks in multimodal fake news detection. 🧠 Beyond Consistency: The Emotional Gap in Fake News Traditionally, models focus on image-text consistency: does the photo match the caption? But this misses the forest for the trees. Fake news isn’t just mismatched—it’s emotionally manipulative. ...