Computer Vision seminars

Two studies examine how diffusion U-Nets model complex images efficiently. Coarse-to-fine conditioning makes fine detail predictable through small, local models, reducing effective dimensionality. At the bottleneck, sparse active channels encode global image information whose geometry reflects semantic similarity even without labels or text conditioning. Self-guided reconstruction probes that representation, supporting an account that combines compact global selection with locally conditioned completion.

Standard diffusion training estimates the mean of clean data conditioned on a noisy sample. This talk instead learns the entire conditional distribution using a scoring-rule objective. Sampling that richer posterior permits larger reverse-process steps and fewer inference iterations. Experiments with images and robot trajectories assess the resulting speed–quality trade-off and compare it with conventional diffusion models.

Learning objects without supervision remains difficult outside restricted settings despite their central role in human vision. This talk argues that assumptions built into many object-centred models conflict with real-world data, making the usual problem formulation inadequate at scale. It examines that mismatch and presents ongoing work intended to support more meaningful learned representations.

Behaviours that resemble world knowledge do not by themselves reveal what an AI represents internally. This talk uses Craik's account of mental models—supporting alternatives, prediction and transfer—to frame empirical tests. Spatial reasoning in language models and motion forecasting in visual models illustrate ways to probe those abilities, along with the limits of inferring world models from systems trained on human-produced representations.

1. Can we reconstruct images that a person saw, directly from their fMRI brain recordings? 2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network? The answer to both of these intriguing questions is “Yes!” In this talk I will present some of our work in both domains. I will then show how combining the power of Brains and Machines can lead to significant breakthroughs in both areas, and potentially bridge the gap between Minds and Machines. Finally, I will show how combining the power of Multiple Brains (with NO shared data) may lead to new breakthrough discoveries in Brain-Science, and allow mapping of information between different brains. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-02-18. Recording duration: 00:50:24.

Over the past decade, neuroscience, cognitive science and computer science (“AI”) converged to create specific, image-computable, deep neural network models intended to appropriately abstract, emulate and explain the mechanisms of primate ventral visual processing, up to its deepest neural level, the inferior temporal cortex (IT). Because these leading neuroscientific emulation models — aka “digital twins” — are fully observable and machine-executable, they offer predictive and potential application power that our field’s prior conceptual models did not. Our team’s ongoing work is aimed at asking if current digital twin models might support non-invasive, beneficial brain modulation. In this talk, I will describe a key result: we demonstrate that we can use a digital twin to design spatial patterns of light energy that, when “added” to the organism’s retinal input in the context of ongoing natural visual processing, results in precise modulation (i.e. rate bias) of the pattern of a population of IT neurons (where any intended modulation pattern is chosen ahead of time by the scientist). Because the IT visual neural populations are known to directly connect to and modulate downstream neural circuits (e.g. amygdala) that may underlie psychological affective states (e.g. mood and anxiety), this novel basic science may unlock a new, non-invasive application avenue of potential future human clinical benefit. This progress and new impact possibilities resulted from convergent brain science and AI engineering efforts in the domain of visual object intelligence. I will motivate this as just one example of what I believe will unlock in other domains of human intelligence as brain scientists and AI engineers collaborate to develop machine-executable models of the underlying mechanisms of those still-mysterious domains. CARL VAN VREESWIJK MEMORIAL LECTURE 2025. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-04-09. Recording duration: 00:55:49.