Search Results for author: James Thewlis

Found 10 papers, 4 papers with code

Learning Context-Adapted Video-Text Retrieval by Attending to User Comments

no code implementations29 Sep 2021 Laura Hanu, Yuki M Asano, James Thewlis, Christian Rupprecht

Learning strong representations for multi-modal retrieval is an important problem for many applications, such as recommendation and search.

Video-Text Retrieval

Unsupervised Learning of Landmarks by Descriptor Vector Exchange

1 code implementation ICCV 2019 James Thewlis, Samuel Albanie, Hakan Bilen, Andrea Vedaldi

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.

Unsupervised Facial Landmark Detection

Deep Industrial Espionage

no code implementations1 Apr 2019 Samuel Albanie, James Thewlis, Sebastien Ehrhardt, Joao Henriques

The theory of deep learning is now considered largely solved, and is well understood by researchers and influencers alike.

Modelling and unsupervised learning of symmetric deformable object categories

no code implementations NeurIPS 2018 James Thewlis, Hakan Bilen, Andrea Vedaldi

We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input.

Frame

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

no code implementations15 Jul 2018 Aravindh Mahendran, James Thewlis, Andrea Vedaldi

We propose a novel method for learning convolutional neural image representations without manual supervision.

Image Classification Optical Flow Estimation +2

Substitute Teacher Networks: Learning with Almost No Supervision

1 code implementation1 Apr 2018 Samuel Albanie, James Thewlis, Joao F. Henriques

Learning through experience is time-consuming, inefficient and often bad for your cortisol levels.

Unsupervised learning of object frames by dense equivariant image labelling

no code implementations NeurIPS 2017 James Thewlis, Hakan Bilen, Andrea Vedaldi

One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations.

Frame Optical Flow Estimation +1

Fully-Trainable Deep Matching

1 code implementation12 Sep 2016 James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching.

Semantic Segmentation

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