Search Results for author: Damien Teney

Found 23 papers, 7 papers with code

Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models

1 code implementation ICCV 2021 Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, Stephen Gould

We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion.

Image Retrieval Visual Reasoning

Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization

1 code implementation12 May 2021 Damien Teney, Ehsan Abbasnejad, Simon Lucey, Anton Van Den Hengel

The method - the first to evade the simplicity bias - highlights the need for a better understanding and control of inductive biases in deep learning.

Model Selection

Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge

no code implementations EACL (LANTERN) 2021 Violetta Shevchenko, Damien Teney, Anthony Dick, Anton Van Den Hengel

The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models.

Question Answering Visual Question Answering +1

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

no code implementations NeurIPS 2020 Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton Van Den Hengel

The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments.

Embodied Question Answering Question Answering +1

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

no code implementations NeurIPS 2020 Damien Teney, Kushal Kafle, Robik Shrestha, Ehsan Abbasnejad, Christopher Kanan, Anton Van Den Hengel

Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set.

Model Selection Question Answering +1

Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

no code implementations ECCV 2020 Damien Teney, Ehsan Abbasnedjad, Anton Van Den Hengel

One of the primary challenges limiting the applicability of deep learning is its susceptibility to learning spurious correlations rather than the underlying mechanisms of the task of interest.

Multi-Label Image Classification Natural Language Inference +3

Unshuffling Data for Improved Generalization

no code implementations27 Feb 2020 Damien Teney, Ehsan Abbasnejad, Anton Van Den Hengel

subsets treated as multiple training environments can guide the learning of models with better out-of-distribution generalization.

Data Augmentation Question Answering +1

On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints

no code implementations30 Sep 2019 Damien Teney, Ehsan Abbasnejad, Anton Van Den Hengel

We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.

Data Augmentation Question Answering +1

On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints

no code implementations25 Sep 2019 Damien Teney, Ehsan Abbasnejad, Anton Van Den Hengel

We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.

Data Augmentation Question Answering +1

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

no code implementations29 Jul 2019 Damien Teney, Peng Wang, Jiewei Cao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.

Visual Reasoning

Actively Seeking and Learning from Live Data

no code implementations CVPR 2019 Damien Teney, Anton Van Den Hengel

One of the key limitations of traditional machine learning methods is their requirement for training data that exemplifies all the information to be learned.

Domain Adaptation Meta-Learning +2

Visual Question Answering as a Meta Learning Task

no code implementations ECCV 2018 Damien Teney, Anton Van Den Hengel

At test time, the method is provided with a support set of example questions/answers, over which it reasons to resolve the given question.

Meta-Learning Question Answering +1

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

7 code implementations CVPR 2018 Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, Anton Van Den Hengel

This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.

Translation Vision and Language Navigation +2

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

59 code implementations CVPR 2018 Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, Lei Zhang

Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning.

Image Captioning Visual Question Answering

Zero-Shot Visual Question Answering

no code implementations17 Nov 2016 Damien Teney, Anton Van Den Hengel

Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions.

Question Answering Visual Question Answering +1

Visual Question Answering: A Survey of Methods and Datasets

1 code implementation20 Jul 2016 Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, Anton Van Den Hengel

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities.

Visual Question Answering

Learning to Extract Motion from Videos in Convolutional Neural Networks

no code implementations27 Jan 2016 Damien Teney, Martial Hebert

Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation.

Motion Estimation Optical Flow Estimation

Learning Similarity Metrics for Dynamic Scene Segmentation

no code implementations CVPR 2015 Damien Teney, Matthew Brown, Dmitry Kit, Peter Hall

This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach.

Metric Learning Motion Segmentation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.