3.5k citations · 14.1k across the 436 of their papers we have counts for
28 papers · 1 filter
Learning Regional Attraction for Line Segment Detection
Nan Xue, Song Bai, Fu-Dong Wang +4
This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map…
Transflow Learning: Repurposing Flow Models Without Retraining
Andrew Gambardella, Atılım Güneş Baydin, Philip H. S. Torr
It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space. Recently, archi…
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Hao Tang, Dan Xu, Yan Yan +2
In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed loc…
Lessons from reinforcement learning for biological representations of space
Alex Muryy, N. Siddharth, Nantas Nardelli +2
Neuroscientists postulate 3D representations in the brain in a variety of different coordinate frames (e.g. 'head-centred', 'hand-centred' and 'world-based'). Recent advances in re…
ManiGAN: Text-Guided Image Manipulation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving ot…
Domain-invariant Stereo Matching Networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang +3
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast,…
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Yuge Shi, N. Siddharth, Brooks Paige +1
Learning generative models that span multiple data modalities, such as vision and language, is often motivated by the desire to learn more useful, generalisable representations tha…
Siam R-CNN: Visual Tracking by Re-Detection
Paul Voigtlaender, Jonathon Luiten, Philip H. S. Torr +1
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with…
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks
Hao Tang, Hong Liu, Dan Xu +2
State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data. Though the existing metho…
Anchor Diffusion for Unsupervised Video Object Segmentation
Zhao Yang, Qiang Wang, Luca Bertinetto +3
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches te…
Mirror Descent View for Neural Network Quantization
Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr +2
Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. It is usually fo…
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation
Arnab Ghosh, Richard Zhang, Puneet K. Dokania +4
We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects. As the user starts to draw a sketch of a desired obj…
Controllable Text-to-Image Generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control part…
Dual Graph Convolutional Network for Semantic Segmentation
Li Zhang, Xiangtai Li, Anurag Arnab +3
Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation. In contrast to previous work that uses multi-scale feature fusion…
Dynamic Graph Message Passing Networks
Li Zhang, Dan Xu, Anurag Arnab +1
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although CNNs have excelled in many vision tasks, they are still limited in capturin…
A Signal Propagation Perspective for Pruning Neural Networks at Initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould +1
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while mi…
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania
Exciting new work on the generalization bounds for neural networks (NN) given by Neyshabur et al. , Bartlett et al. closely depend on two parameter-depenedent quantities: the Lipsc…
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr
Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks…
Straight to Shapes++: Real-time Instance Segmentation Made More Accurate
Laurynas Miksys, Saumya Jetley, Michael Sapienza +2
Instance segmentation is an important problem in computer vision, with applications in autonomous driving, drone navigation and robotic manipulation. However, most existing methods…
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang +1
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose…
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Eunwoo Kim, Chanho Ahn, Philip H. S. Torr +1
Deep networks consume a large amount of memory by their nature. A natural question arises can we reduce that memory requirement whilst maintaining performance. In particular, in th…
Res2Net: A New Multi-scale Backbone Architecture
Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao +3
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate…
Domain Partitioning Network
Botos Csaba, Adnane Boukhayma, Viveka Kulharia +2
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game. However, even if the players converge to an equilibrium, the gen…
3D Hand Shape and Pose from Images in the Wild
Adnane Boukhayma, Rodrigo de Bem, Philip H. S. Torr
We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild. Our network consists of the concatena…