3.5k citations · 14.1k across the 436 of their papers we have counts for
12 papers · 1 filter
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip H. S. Torr +1
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-…
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
Qinbin Hou, Puneet Kumar Dokania, Daniela Massiceti +3
We consider the task of learning a classifier for semantic segmentation using weak supervision in the form of image labels which specify the object classes present in the image. Ou…
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik +3
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D enviro…
Online Real-time Multiple Spatiotemporal Action Localisation and Prediction
Gurkirt Singh, Suman Saha, Michael Sapienza +2
We present a deep-learning framework for real-time multiple spatio-temporal (S/T) action localisation, classification and early prediction. Current state-of-the-art approaches work…
Straight to Shapes: Real-time Detection of Encoded Shapes
Saumya Jetley, Michael Sapienza, Stuart Golodetz +1
Current object detection approaches predict bounding boxes, but these provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose…
Deeply supervised salient object detection with short connections
Qibin Hou, Ming-Ming Cheng, Xiao-Wei Hu +3
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency d…
Fully-Trainable Deep Matching
James Thewlis, Shuai Zheng, Philip H. S. Torr +1
Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that…
Bottom-up Instance Segmentation using Deep Higher-Order CRFs
Anurag Arnab, Philip H. S. Torr
Traditional Scene Understanding problems such as Object Detection and Semantic Segmentation have made breakthroughs in recent years due to the adoption of deep learning. However, t…
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos
Suman Saha, Gurkirt Singh, Michael Sapienza +2
In this work, we propose an approach to the spatiotemporal localisation (detection) and classification of multiple concurrent actions within temporally untrimmed videos. Our framew…
Learning feed-forward one-shot learners
Luca Bertinetto, João F. Henriques, Jack Valmadre +2
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learn…
Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto, Jack Valmadre, João F. Henriques +2
The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video its…
Joint Object-Material Category Segmentation from Audio-Visual Cues
Anurag Arnab, Michael Sapienza, Stuart Golodetz +4
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very d…