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In this work we present an end-to-end system for text spotting-localising and recognising text in natural scene images-and text based image retrieval. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.

The focus of this paper is speeding up the application of convolutional neural networks (CNNs). While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain. Our methods are architecture agnostic, and can be easily applied to existing CPU and GPU convolutional frameworks for tuneable speedup performance. We demonstrate this with a real world network designed for scene text character recognition [1], showing a possible 2.5× speedup with no loss in accuracy, and 4.5× speedup with less than 1% drop in accuracy, still achieving state-of-the-art on standard benchmarks.Approximation Schemes. We provide the frameworks for two methods to approximate the N 3D filters W n of a convolutional layer acting on the input z with C channels z c n such that W n * z = ∑ C c=1 W c n * z c . Both methods exploit the redundancy that exists between different feature channels and filters of convolutional layers.Scheme 1 builds upon the work of Rigamonti et al.[2] and approximates the original set of full-rank filters as a linear combination of a smaller set of separable (rank-1) filters. The separability of these filters allows convolutions to be computed much more efficiently than the fullrank filters by splitting the full convolution in to horizontal convolution followed by vertical convolution. For a layer of N convolutional filters

Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments (30, 40, 45,46,56) and two-player turn-based games (47,58,66). However, the realworld contains multiple agents, each learning and acting independently to cooperate and compete with other agents, and environments reflecting this degree of complexity remain an open challenge. In this work, we demonstrate for the first time that an agent can achieve human-level in a popular 3D multiplayer first-person video game, Quake III Arena Capture the Flag (28), using only pixels and game points as input. These results were achieved by a novel two-tier optimisation process in which a population of independent RL agents are trained concurrently from thousands of parallel matches with agents playing in teams together and against each other on randomly generated environments. Each agent in the population learns its own internal reward signal to complement the sparse delayed reward from winning, and selects actions using a novel temporally hierarchical representation that enables the agent to reason at multiple timescales. During game-play, these agents display humanlike behaviours such as navigating, following, and defending based on a rich learned representation that is shown to encode high-level game knowledge. In an extensive tournament-style evaluation the trained agents exceeded the winrate of strong human players both as teammates and opponents, and proved far stronger than existing state-of-the-art agents. These results demonstrate a 1 arXiv:1807.01281v1 [cs.LG] 3 Jul 2018 significant jump in the capabilities of artificial agents, bringing us closer to the goal of human-level intelligence.We demonstrate how intelligent behaviour can emerge from training sophisticated new learning agents within complex multi-agent environments. End-to-end reinforcement learning methods (45, 46) have so far not succeeded in training agents in multi-agent games that combine team and competitive play due to the high complexity of the learning problem (7, 43) that arises from the concurrent adaptation of other learning agents in the environment. We approach this challenge by studying team-based multiplayer 3D first-person video games, a genre which is particularly immersive for humans (16) and has even been shown to improve a wide range of cognitive abilities (21). We focus specifically on a modified version (5) of Quake III Arena (28), the canonical multiplayer 3D first-person video game, whose game mechanics served as the basis for many subsequent games, and which has a thriving professional scene (1). The task we consider is the game mode Capture the Flag (CTF) on per game randomly generated maps of both indoor and outdoor theme ( Figure 1 (a,b)). Two opposing teams consisting of multiple individual players compete to capture each other's flags by strategically navigating, tagging, and evading opponents. The team with the greatest number of flag captures after five minutes wins. CTF is play...

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