http://hdl.handle.net/2440/115881 2020-02-27T12:00:31Z 2020-02-27T12:00:31Z Zhang, L. Wang, P. Shen, C. Liu, L. Wei, W. Zhang, Y. van den Hengel, A. http://hdl.handle.net/2440/123111 2020-02-25T12:17:08Z 2019-01-01T00:00:00Z

Title: Adaptive importance learning for improving lightweight image super-resolution network Author: Zhang, L.; Wang, P.; Shen, C.; Liu, L.; Wei, W.; Zhang, Y.; van den Hengel, A. Abstract: Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that balance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthogonal view, and propose a novel learning strategy to maximize the pixel-wise fitting ability of a given lightweight network architecture. Considering that the initial performance of the lightweight network is very limited, we present an adaptive importance learning scheme for SISR that trains the network with an easy-to-complex paradigm by dynamically updating the importance of image pixels on the basis of the training loss. Specifically, we formulate the network training and the importance learning into a joint optimization problem. With a carefully designed importance penalty function, the importance of individual pixels can be gradually increased through solving a convex optimization problem. The training process thus begins with pixels that are easy to reconstruct, and gradually proceeds to more complex pixels as fitting improves. Furthermore, the proposed learning scheme is able to seamlessly assimilate knowledge from a more powerful teacher network in the form of importance initialization, thus obtaining better initial performance for the network. Through learning the network parameters, and updating pixel importance, the proposed learning scheme enables smaller, lightweight, networks to achieve better performance than has previously been possible. Extensive experiments on four benchmark datasets demonstrate the potential benefits of the proposed learning strategy in lightweight SISR network enhancement. In some cases, our learned network with only 25% of the parameters and computational complexity can produce comparable or even better results than the corresponding full-parameter network.

2019-01-01T00:00:00Z Pang, G. Shen, C. Van Den Hengel, A. http://hdl.handle.net/2440/122748 2020-01-17T01:37:44Z 2019-01-01T00:00:00Z

Title: Deep anomaly detection with deviation networks Author: Pang, G.; Shen, C.; Van Den Hengel, A. Abstract: Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications. This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

2019-01-01T00:00:00Z Do, T. Nguyen, A. Reid, I. http://hdl.handle.net/2440/120065 2019-07-15T23:27:46Z 2018-01-01T00:00:00Z

Title: AffordanceNet: an end-to-end deep learning approach for object affordance detection Author: Do, T.; Nguyen, A.; Reid, I. Abstract: We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at https://github.com/nqanh/affordance-net.

2018-01-01T00:00:00Z Zhuang, B. Wu, Q. Shen, C. Reid, I. van den Hengel, A. http://hdl.handle.net/2440/120064 2020-01-06T04:15:15Z 2018-01-01T00:00:00Z

Title: Parallel attention: a unified framework for visual object discovery through dialogs and queries Author: Zhuang, B.; Wu, Q.; Shen, C.; Reid, I.; van den Hengel, A. Abstract: Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision. There are a great many practical applications where the subjects that may be of interest are not known beforehand, or so easily delineated, however. In many of these cases natural language dialog is a natural way to specify the subject of interest, and the task achieving this capability (a.k.a, Referring Expression Comprehension) has recently attracted attention. To this end we propose a unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs. The PLAN network has two attention mechanisms that relate parts of the expressions to both the global visual content and also directly to object candidates. Furthermore, the attention mechanisms are recurrent, making the referring process visualizable and explainable. The attended information from these dual sources are combined to reason about the referred object. These two attention mechanisms can be trained in parallel and we find the combined system outperforms the state-of-art on several benchmarked datasets with different length language input, such as RefCOCO, RefCOCO+ and GuessWhat?!.

2018-01-01T00:00:00Z