105 citations · 436 across the 29 of their papers we have counts for
29 papers
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Mohammadreza Salehi, Efstratios Gavves, Cees G. M. Snoek +1
Spatially dense self-supervised learning is a rapidly growing problem domain with promising applications for unsupervised segmentation and pretraining for dense downstream tasks. D…
Efficient Neural PDE-Solvers using Quantization Aware Training
Winfried van den Dool, Tijmen Blankevoort, Max Welling +1
In the past years, the application of neural networks as an alternative to classical numerical methods to solve Partial Differential Equations has emerged as a potential paradigm s…
Learning to Count without Annotations
Lukas Knobel, Tengda Han, Yuki M. Asano
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost a…
BISCUIT: Causal Representation Learning from Binary Interactions
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI. While an agent can commonly inter…
Self-Ordering Point Clouds
Pengwan Yang, Cees G. M. Snoek, Yuki M. Asano
In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this cha…
Towards Label-Efficient Incremental Learning: A Survey
Mert Kilickaya, Joost van de Weijer, Yuki M. Asano
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assume…
Skip-Attention: Improving Vision Transformers by Paying Less Attention
Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano +2
This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these ope…
VTC: Improving Video-Text Retrieval with User Comments
Laura Hanu, James Thewlis, Yuki M. Asano +1
Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and cons…
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers
Jochem Loedeman, Maarten C. Stol, Tengda Han +1
With the introduction of the transformer architecture in computer vision, increasing model scale has been demonstrated as a clear path to achieving performance and robustness gains…
Self-Guided Diffusion Models
Vincent Tao Hu, David W Zhang, Yuki M. Asano +2
Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process. However, guidance requires a…
Measuring the Interpretability of Unsupervised Representations via Quantized Reverse Probing
Iro Laina, Yuki M. Asano, Andrea Vedaldi
Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through trans…
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has sh…
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
Conrad Borchers, Dalia Sara Gala, Benjamin Gilburt +4
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated b…
Less than Few: Self-Shot Video Instance Segmentation
Pengwan Yang, Yuki M. Asano, Pascal Mettes +1
The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labell…
Self-Supervised Learning of Object Parts for Semantic Segmentation
Adrian Ziegler, Yuki M. Asano
Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks suc…
CITRIS: Causal Identifiability from Temporal Intervened Sequences
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Understanding the latent causal factors of a dynamical system from visual observations is considered a crucial step towards agents reasoning in complex environments. In this paper,…
The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image
Yuki M. Asano, Aaqib Saeed
What can neural networks learn about the visual world when provided with only a single image as input? While any image obviously cannot contain the multitudes of all existing objec…
PASS: An ImageNet replacement for self-supervised pretraining without humans
Yuki M. Asano, Christian Rupprecht, Andrew Zisserman +1
Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical…
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
Hannah Rose Kirk, Yennie Jun, Paulius Rauba +7
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released…
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Mandela Patrick, Dylan Campbell, Yuki M. Asano +5
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions. However, in a scene where objects or the camera may move, a physical point…
Self-supervised object detection from audio-visual correspondence
Triantafyllos Afouras, Yuki M. Asano, Francois Fagan +2
We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we…
Privacy-preserving Object Detection
Peiyang He, Charlie Griffin, Krzysztof Kacprzyk +4
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to p…
Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning
Mandela Patrick, Yuki M. Asano, Bernie Huang +4
The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers h…
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Hannah Kirk, Yennie Jun, Haider Iqbal +5
The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these…