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Multi-View Stereo via Volumetric Graph-Cuts
This paper presents a novel formulation for the multi-view scene reconstruction problem. While this formulation benefits from a volumetric scene representation, it is amenable to a computationally tractable global optimisation using Graph-cuts. The algorithm proposed uses the visual hull of…
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Goal Density-based Hindsight Experience Prioritization for Multi-Goal Robot Manipulation Reinforcement Learning
Reinforcement learning for multi-goal robot manipulation tasks is usually challenging, especially when sparse rewards are provided. It often requires millions of data collected before a stable strategy is learned. Recent algorithms like Hindsight Experience Replay (HER) have accelerated the learning…
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CSC-GAN: Cycle and Semantic Consistency for Dataset Augmentation
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Large Scale Multi-view Stereopsis Evaluation
The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes.…
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A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization
The efficiency of traffic flows in urban areas largely depends on signal operation. The state-of-the-art traffic signal control strategies are not able to efficiently deal with varying or over-saturated conditions. To optimize the performance of existing traffic signal infrastructure, we…
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Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo
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Domain Adaptation for Reinforcement Learning on the Atari
Deep Reinforcement learning is a powerful machine learning paradigm that has had significant success across a wide range of control problems. This success often requires long training times to achieve. Observing that many problems share similarities, it is likely that…
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Non-rigid Photometric Stereo with Colored Lights
We present an algorithm and the associated capture methodology to acquire and track the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured…
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Asymmetric Spatio-Temporal Embeddings for Large-Scale Image-to-Video Retrieval
We address the problem of image-to-video retrieval. Given a query image, the aim is to identify the frame or scene within a collection of videos that best matches the visual input. Matching images to videos is an asymmetric task in…
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A probabilistic model for trust and reputation
This paper concerns the problem of agent trust in an electronic market place. We maintain that agent trust involves making decisions under uncertainty and therefore the phenomenon should be modelled probabilistically. We therefore propose a probabilistic framework that models agent…
