Modeling Extremely Large Images with xT – The Berkeley Artificial Intelligence Research Blog
Posted inArtificial Intelligence

Modeling Extremely Large Images with xT – The Berkeley Artificial Intelligence Research Blog



As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. We take another look at these approaches and introduce $x$T, a new framework to model large images end-to-end on contemporary GPUs while effectively aggregating global context with local details.



Architecture for the $x$T framework.

Posted inArtificial Intelligence

The Visual Haystacks Benchmark! – The Berkeley Artificial Intelligence Research Blog



Humans excel at processing vast arrays of visual information, a skill that is crucial for achieving artificial general intelligence (AGI). Over the decades, AI researchers have developed Visual Question Answering (VQA) systems to interpret scenes within single images and answer related questions. While recent advancements in foundation models have significantly closed the gap between human and machine visual processing, conventional VQA has been restricted to reason about only single images at a time rather than whole collections of visual data.

This limitation poses challenges in more complex scenarios. Take, for example, the challenges of discerning patterns in collections of medical images, monitoring deforestation through satellite imagery, mapping urban changes using autonomous navigation data, analyzing thematic elements across large art collections, or understanding consumer behavior from retail surveillance footage. Each of these scenarios entails not only visual processing across hundreds or thousands of images but also necessitates cross-image processing of these findings. To address this gap, this project focuses on the “Multi-Image Question Answering” (MIQA) task, which exceeds the reach of traditional VQA systems.



Visual Haystacks: the first “visual-centric” Needle-In-A-Haystack (NIAH) benchmark designed to rigorously evaluate Large Multimodal Models (LMMs) in processing long-context visual information.