research
∙
06/05/2023
Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fields
We introduce Explicit Neural Surfaces (ENS), an efficient surface recons...
share
research
∙
03/27/2023
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
The goal of this work is to understand the way actions are performed in ...
research
∙
12/11/2022
Accelerating Self-Supervised Learning via Efficient Training Strategies
Recently the focus of the computer vision community has shifted from exp...
research
∙
12/01/2022
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimatio...
research
∙
12/01/2022
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Understanding the 3D world without supervision is currently a major chal...
research
∙
11/26/2022
Who are you referring to? Weakly supervised coreference resolution with multimodal grounding
Coreference resolution aims at identifying words and phrases which refer...
research
∙
11/17/2022
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Diffusion models currently achieve state-of-the-art performance for both...
research
∙
11/06/2022
Learning to Annotate Part Segmentation with Gradient Matching
The success of state-of-the-art deep neural networks heavily relies on t...
research
∙
11/06/2022
Distilling Representations from GAN Generator via Squeeze and Span
In recent years, generative adversarial networks (GANs) have been an act...
research
∙
05/23/2022
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Face recognition, as one of the most successful applications in artifici...
research
∙
04/15/2022
Synthesizing Informative Training Samples with GAN
Remarkable progress has been achieved in synthesizing photo-realistic im...
research
∙
04/06/2022
Universal Representations: A Unified Look at Multiple Task and Domain Learning
We propose a unified look at jointly learning multiple vision tasks and ...
research
∙
03/31/2022
3D Equivariant Graph Implicit Functions
In recent years, neural implicit representations have made remarkable pr...
research
∙
03/03/2022
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort throug...
research
∙
11/29/2021
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Despite the recent advances in multi-task learning of dense prediction p...
research
∙
11/26/2021
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation
Scene graph generation (SGG) aims to capture a wide variety of interacti...
research
∙
10/08/2021
Dataset Condensation with Distribution Matching
Computational cost to train state-of-the-art deep models in many learnin...
research
∙
07/01/2021
Improving Task Adaptation for Cross-domain Few-shot Learning
In this paper, we look at the problem of cross-domain few-shot classific...
research
∙
04/02/2021
Semi-supervised Viewpoint Estimation with Geometry-aware Conditional Generation
There is a growing interest in developing computer vision methods that c...
research
∙
04/01/2021
Unsupervised Foreground-Background Segmentation with Equivariant Layered GANs
We propose an unsupervised foreground-background segmentation method via...
research
∙
03/25/2021
Universal Representation Learning from Multiple Domains for Few-shot Classification
In this paper, we look at the problem of few-shot classification that ai...
research
∙
02/16/2021
Dataset Condensation with Differentiable Siamese Augmentation
In many machine learning problems, large-scale datasets have become the ...
research
∙
10/05/2020
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it...
research
∙
07/14/2020
Knowledge Distillation for Multi-task Learning
Multi-task learning (MTL) is to learn one single model that performs mul...
research
∙
06/10/2020
Dataset Condensation with Gradient Matching
Efficient training of deep neural networks is an increasingly important ...
research
∙
06/08/2020
Continual Representation Learning for Biometric Identification
With the explosion of digital data in recent years, continuously learnin...
research
∙
06/01/2020
Latent Domain Learning with Dynamic Residual Adapters
A practical shortcoming of deep neural networks is their specialization ...
research
∙
01/08/2020
iDLG: Improved Deep Leakage from Gradients
It is widely believed that sharing gradients will not leak private train...
research
∙
12/22/2019
Learning to Impute: A General Framework for Semi-supervised Learning
Recent semi-supervised learning methods have shown to achieve comparable...
research
∙
11/22/2019
Learning to Caption Images with Two-Stream Attention and Sentence Auto-Encoder
Automatically generating natural language descriptions from an image is ...
research
∙
10/19/2019
NormGrad: Finding the Pixels that Matter for Training
The different families of saliency methods, either based on contrastive ...
research
∙
10/18/2019
Image Deconvolution with Deep Image and Kernel Priors
Image deconvolution is the process of recovering convolutional degraded ...
research
∙
08/18/2019
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
Equivariance to random image transformations is an effective method to l...
research
∙
07/08/2019
Personalised aesthetics with residual adapters
The use of computational methods to evaluate aesthetics in photography h...
research
∙
07/03/2019
Learning Landmarks from Unaligned Data using Image Translation
We introduce a method for learning landmark detectors from unlabelled vi...
research
∙
04/16/2019
Weakly Supervised Gaussian Networks for Action Detection
Detecting temporal extents of human actions in videos is a challenging c...
research
∙
10/12/2018
Mode Normalization
Normalization methods are a central building block in the deep learning ...
research
∙
06/20/2018
Conditional Image Generation for Learning the Structure of Visual Objects
In this paper, we consider the problem of learning landmarks for object ...
research
∙
03/27/2018
Efficient parametrization of multi-domain deep neural networks
A practical limitation of deep neural networks is their high degree of s...
research
∙
06/09/2017
Unsupervised learning of object frames by dense equivariant image labelling
One of the key challenges of visual perception is to extract abstract mo...
research
∙
05/22/2017
Learning multiple visual domains with residual adapters
There is a growing interest in learning data representations that work w...
research
∙
05/05/2017
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an imp...
research
∙
01/25/2017
Universal representations:The missing link between faces, text, planktons, and cat breeds
With the advent of large labelled datasets and high-capacity models, the...
research
∙
12/02/2016
Action Recognition with Dynamic Image Networks
We introduce the concept of "dynamic image", a novel compact representat...
research
∙
11/21/2016
Self-Supervised Video Representation Learning With Odd-One-Out Networks
We propose a new self-supervised CNN pre-training technique based on a n...
research
∙
06/06/2016
Integrated perception with recurrent multi-task neural networks
Modern discriminative predictors have been shown to match natural intell...
research
∙
11/09/2015