Search Results for author:
Found 12 papers, 5 papers with code
Language as the Medium: Multimodal Video Classification through text only
Despite an exciting new wave of multimodal machine learning models, current approaches still struggle to interpret the complex contextual relationships between the different modalities present in videos.
VTC: Improving Video-Text Retrieval with User Comments
In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations.
Learning Context-Adapted Video-Text Retrieval by Attending to User Comments
Learning strong representations for multi-modal retrieval is an important problem for many applications, such as recommendation and search.
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
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.
Ranked #1 on
Unsupervised Facial Landmark Detection
on 300W
Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates.
Deep Industrial Espionage
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
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.
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image representations without manual supervision.
Substitute Teacher Networks: Learning with Almost No Supervision
Learning through experience is time-consuming, inefficient and often bad for your cortisol levels.
Unsupervised learning of object frames by dense equivariant image labelling
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.
Ranked #3 on
Unsupervised Facial Landmark Detection
on AFLW-MTFL
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an important open problem in computer vision.
Ranked #2 on
Unsupervised Facial Landmark Detection
on AFLW-MTFL
Fully-Trainable Deep Matching
Deep Matching (DM) is a popular high-quality method for quasi-dense image matching.
