562 citations · 1.6k across the 39 of their papers we have counts for
44 papers
Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fields
Thomas Walker, Octave Mariotti, Amir Vaxman +1
We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain. We apply t…
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
Davide Moltisanti, Frank Keller, Hakan Bilen +1
The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action…
Accelerating Self-Supervised Learning via Efficient Training Strategies
Mustafa Taha Koçyiğit, Timothy M. Hospedales, Hakan Bilen
Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations. While the performance…
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF i…
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are…
Learning to Annotate Part Segmentation with Gradient Matching
Yu Yang, Xiaotian Cheng, Hakan Bilen +1
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.…
Distilling Representations from GAN Generator via Squeeze and Span
Yu Yang, Xiaotian Cheng, Chang Liu +2
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The…
Who are you referring to? Coreference resolution in image narrations
Arushi Goel, Basura Fernando, Frank Keller +1
Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing. In this paper, we extend this task to re…
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Kai Wang, Bo Zhao, Xiangyu Peng +5
Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the…
Synthesizing Informative Training Samples with GAN
Bo Zhao, Hakan Bilen
Remarkable progress has been achieved in synthesizing photo-realistic images with generative adversarial networks (GANs). Recently, GANs are utilized as the training sample generat…
Universal Representations: A Unified Look at Multiple Task and Domain Learning
Wei-Hong Li, Xialei Liu, Hakan Bilen
We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network. Learning multiple problems s…
3D Equivariant Graph Implicit Functions
Yunlu Chen, Basura Fernando, Hakan Bilen +2
In recent years, neural implicit representations have made remarkable progress in modeling of 3D shapes with arbitrary topology. In this work, we address two key limitations of suc…
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Wei-Hong Li, Xialei Liu, Hakan Bilen
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient appro…
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation
Arushi Goel, Basura Fernando, Frank Keller +1
Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained…
Dataset Condensation with Distribution Matching
Bo Zhao, Hakan Bilen
Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising…
Cross-domain Few-shot Learning with Task-specific Adapters
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. R…
Semi-supervised Viewpoint Estimation with Geometry-aware Conditional Generation
Octave Mariotti, Hakan Bilen
There is a growing interest in developing computer vision methods that can learn from limited supervision. In this paper, we consider the problem of learning to predict camera view…
Learning Foreground-Background Segmentation from Improved Layered GANs
Yu Yang, Hakan Bilen, Qiran Zou +2
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In…
Universal Representation Learning from Multiple Domains for Few-shot Classification
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods…
Dataset Condensation with Differentiable Siamese Augmentation
Bo Zhao, Hakan Bilen
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this pa…
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen +1
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality. In th…
Knowledge Distillation for Multi-task Learning
Wei-Hong Li, Hakan Bilen
Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model…
Continual Representation Learning for Biometric Identification
Bo Zhao, Shixiang Tang, Dapeng Chen +2
With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly i…