232 citations · 338 across the 21 of their papers we have counts for
26 papers
KilometerVision: A New Frontier for Large-Scale Spatial Intelligence in VLMs
Aravindh Mahendran, Michael King, Matthew Koichi Grimes +12
We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical layout understanding from real-…
The Computational Basis of Confidence in Large Language Models
Dharshan Kumaran, Viorica Patraucean, Maks Ovsjanikov +2
Reliable confidence -- the probability that a model's own answer is correct -- is essential for the trustworthy deployment of language models. Existing work has largely evaluated c…
SeeSE3: Emergence of 3D Space in Vision Features
Caroline Chen, Sayna Ebrahimi, Fedor Kitashov +4
In this paper, we ask whether vision foundation models construct representations that reflect the intrinsic properties of 3D Euclidean space. Unlike previous works that probe 3D aw…
Gen4U: Unifying Video Generation and Understanding via Diffusion
Michael King, Aravindh Mahendran, Matthew Koichi Grimes +5
Prior work suggests that diffusion representations capture low-level geometry but struggle with high-level semantics. We demonstrate that state-of-the-art video diffusion models ov…
How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals
Dharshan Kumaran, Viorica Patraucean, Simon Osindero +2
Large language models can detect their own errors and sometimes correct them without external feedback, but the underlying mechanisms remain unknown. We investigate this through th…
Causal Evidence that Language Models use Confidence to Drive Behavior
Dharshan Kumaran, Nathaniel Daw, Simon Osindero +2
Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstrates that confidence signals can…
How do LLMs Compute Verbal Confidence
Dharshan Kumaran, Arthur Conmy, Federico Barbero +3
Verbal confidence -- prompting LLMs to state their confidence as a number or category -- is widely used to extract uncertainty estimates from black-box models. However, how LLMs in…
Perception Test 2025: Challenge Summary and a Unified VQA Extension
Joseph Heyward, Nikhil Parthasarathy, Tyler Zhu +5
The Third Perception Test challenge was organised as a full-day workshop alongside the IEEE/CVF International Conference on Computer Vision (ICCV) 2025. Its primary goal is to benc…
Unique Lives, Shared World: Learning from Single-Life Videos
Tengda Han, Sayna Ebrahimi, Dilara Gokay +8
We introduce the "single-life" learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual. We leverage the multiple viewp…
Dynamic Reflections: Probing Video Representations with Text Alignment
Tyler Zhu, Tengda Han, Leonidas Guibas +2
The alignment of representations from different modalities has recently been shown to provide insights on the structural similarities and downstream capabilities of different encod…
How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models
Dharshan Kumaran, Stephen M Fleming, Larisa Markeeva +8
Large language models (LLMs) exhibit strikingly conflicting behaviors: they can appear steadfastly overconfident in their initial answers whilst at the same time being prone to exc…
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus, Carl Doersch, Yi Yang +7
Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods…
Learning from Streaming Video with Orthogonal Gradients
Tengda Han, Dilara Gokay, Joseph Heyward +6
We address the challenge of representation learning from a continuous stream of video as input, in a self-supervised manner. This differs from the standard approaches to video lear…
Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark
Joseph Heyward, João Carreira, Dima Damen +2
Following the successful 2023 edition, we organised the Second Perception Test challenge as a half-day workshop alongside the IEEE/CVF European Conference on Computer Vision (ECCV)…
Perception Test 2023: A Summary of the First Challenge And Outcome
Joseph Heyward, João Carreira, Dima Damen +2
The First Perception Test challenge was held as a half-day workshop alongside the IEEE/CVF International Conference on Computer Vision (ICCV) 2023, with the goal of benchmarking st…
A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames
Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak +7
Understanding long, real-world videos requires modeling of long-range visual dependencies. To this end, we explore video-first architectures, building on the common paradigm of tra…
Learning from One Continuous Video Stream
João Carreira, Michael King, Viorica Pătrăucean +9
We introduce a framework for online learning from a single continuous video stream -- the way people and animals learn, without mini-batches, data augmentation or shuffling. This p…
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
Jannik Kossen, Cătălina Cangea, Eszter Vértes +5
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily av…
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling
Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz +2
How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the…
Broaden Your Views for Self-Supervised Video Learning
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac +11
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by…
Sideways: Depth-Parallel Training of Video Models
Mateusz Malinowski, Grzegorz Swirszcz, Joao Carreira +1
We propose Sideways, an approximate backpropagation scheme for training video models. In standard backpropagation, the gradients and activations at every computation step through t…
Massively Parallel Video Networks
Joao Carreira, Viorica Patraucean, Laurent Mazare +2
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number…
gvnn: Neural Network Library for Geometric Computer Vision
Ankur Handa, Michael Bloesch, Viorica Patraucean +3
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of S…
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan +2
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning…