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October 1, 2021 Hung Le, Chinnadhurai Sankar, Seungwhan Moon, Ahmad Beirami, Alborz Geramifard, Satwik Kottur
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DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue

The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations.
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August 31, 2021 Chunyang Wu, Zhiping Xiu, Yangyang Shi, Ozlem Kalinli, Christian Fuegen, Thilo Koehler, Qing He
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Transformer-based Acoustic Modeling for Streaming Speech Synthesis

To address the complexity issue in speech synthesis domain, this paper proposes an efficient transformer-based acoustic model that is constant-speed regardless of input sequence length, making it ideal for streaming speech synthesis applications.
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August 29, 2021 Anurag Kumar, Yun Wang, Vamsi Krishna Ithapu, Christian Fuegen
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Do Sound Event Representations Generalize To Other Audio Tasks? A Case Study In Audio Transfer Learning

In this paper, we investigate the transfer learning capacity of audio representations obtained from neural networks trained on a large-scale sound event detection dataset.
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August 23, 2021 Jacob Donley, Vladimir Tourbabin, Boaz Rafaely, Ravish Mehra
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Adaptive Multi-Channel Signal Enhancement Based on Multi-Source Contribution Estimation

This paper outlines a new method to adapt to desired and undesired signals using their spatial statistics, independent of their temporal characteristics. The method uses a linearly constrained minimum variance (LCMV) beamformer to estimate the relative source contribution of each source in a mixture, which is then used to weight statistical estimates of the spatial characteristics of each source used for final separation.
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August 20, 2021 Virginie Do, Jamal Atif, Jérôme Lang, Nicolas Usunier
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Online Selection of Diverse Committees

We study three methods, theoretically and experimentally: a greedy algorithm that includes volunteers as long as proportionality is not violated; a non-adaptive method that includes a volunteer with a probability depending only on their features, assuming that the joint feature distribution in the volunteer pool is known; and a reinforcement learning based approach when this distribution is not known a priori but learnt online.
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August 13, 2021 Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg
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Preference Amplification in Recommender Systems

We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually “drift” toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences.
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August 9, 2021 Sai Bi, Stephen Lombardi, Shunsuke Saito, Tomas Simon, Shih-En Wei, Kevyn Mcphail, Ravi Ramamoorthi, Yaser Sheikh, Jason Saragih
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Deep Relightable Appearance Models for Animatable Faces

We present a method for building high-fidelity animatable 3D face models that can be posed and rendered with novel lighting environments in real-time.
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August 9, 2021 Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, Jason Saragih
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Mixture of Volumetric Primitives for Efficient Neural Rendering

We present Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, e.g., point-based or mesh-based methods. Our approach achieves this by leveraging spatially shared computation with a convolutional architecture and by minimizing computation in empty regions of space with volumetric primitives that can move to cover only occupied regions.
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August 9, 2021 Timur Bagautdinov, Chenglei Wu, Tomas Simon, Fabián Prada, Takaaki Shiratori, Shih-En Wei, Weipeng Xu, Yaser Sheikh, Jason Saragih
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Driving-Signal Aware Full-Body Avatars

The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.
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August 9, 2021 Jungdam Won, Deepak Gopinath, Jessica Hodgins
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Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many degrees-of-freedom. Our framework uses a two step-approach, learning basic skills and learning boutlevel strategies, with deep reinforcement learning, which is inspired by the way that people how to learn competitive sports.
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