Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part V
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Chapter and Conference Paper
Rethinking Learning Approaches for Long-Term Action Anticipation
Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoin...
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Article
Guest Editorial: Special Issue on ACCV 2018
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Chapter and Conference Paper
House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph ...
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Chapter and Conference Paper
Generating Videos of Zero-Shot Compositions of Actions and Objects
Human activity videos involve rich, varied interactions between people and objects. In this paper we develop methods for generating such videos – making progress toward addressing the important, open problem o...
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Chapter and Conference Paper
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
Humans accumulate knowledge in a lifelong fashion. Modern deep neural networks, on the other hand, are susceptible to catastrophic forgetting: when adapted to perform new tasks, they often fail to preserve the...
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Book and Conference Proceedings
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Book and Conference Proceedings
Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part VI
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Book and Conference Proceedings
Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part II
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Book and Conference Proceedings
Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part I
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Book and Conference Proceedings
Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III
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Chapter and Conference Paper
Deep Learning of Appearance Models for Online Object Tracking
This paper introduces a deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the input video frames and directly compute...
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Book and Conference Proceedings
Computer Vision – ACCV 2018
14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part IV
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Article
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a vide...
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Chapter and Conference Paper
Sparsely Aggregated Convolutional Networks
We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers. Such aggregat...
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Chapter and Conference Paper
Probabilistic Video Generation Using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data. A video can be thought of as being governed by two factors: (i) temporally invariant (e.g., person identity), or slowly varying (e.g., act...
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Chapter and Conference Paper
Object Level Visual Reasoning in Videos
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global cont...
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Chapter and Conference Paper
Hierarchical Relational Networks for Group Activity Recognition and Retrieval
Modeling structured relationships between people in a scene is an important step toward visual understanding. We present a Hierarchical Relational Network that computes relational representations of people, gi...
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Chapter and Conference Paper
Constraint-Aware Deep Neural Network Compression
Deep neural network compression has the potential to bring modern resource-hungry deep networks to resource-limited devices. However, in many of the most compelling deployment scenarios of compressed deep netw...
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Article
A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials
Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another ser...
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Chapter and Conference Paper
Learning Action Primitives for Multi-level Video Event Understanding
Human action categories exhibit significant intra-class variation. Changes in viewpoint, human appearance, and the temporal evolution of an action confound recognition algorithms. In order to address this, we ...
