8.9k citations · 11.5k across the 56 of their papers we have counts for
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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-…
TRecViT: A Recurrent Video Transformer
Viorica Pătrăucean, Xu Owen He, Joseph Heyward +10
We propose a novel block for \emph{causal} video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (…
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Viorica Pătrăucean, Lucas Smaira, Ankush Gupta +21
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GP…
CLIP-CLOP: CLIP-Guided Collage and Photomontage
Piotr Mirowski, Dylan Banarse, Mateusz Malinowski +2
The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art. Increasingly more sophisticated generat…
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…
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
Chen-Yu Lee, Simon Osindero
We present recursive recurrent neural networks with attention modeling (RAM) for lexicon-free optical character recognition in natural scene images. The primary advantages of t…
Cross-dimensional Weighting for Aggregated Deep Convolutional Features
Yannis Kalantidis, Clayton Mellina, Simon Osindero
We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer ou…