AI tool generates high-quality images faster than state-of-the-art approaches
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
computer-vision
Streamlining data collection for improved salmon population management
Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.
Expanding robot perception
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
Ecologists find computer vision models’ blind spots in retrieving wildlife images
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
A new way to create realistic 3D shapes using generative AI
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
New AI tool generates realistic satellite images of future flooding
The method could help communities visualize and prepare for approaching storms.
Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.
Combining next-token prediction and video diffusion in computer vision and robotics
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
AI pareidolia: Can machines spot faces in inanimate objects?
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.