Eadom Dessalene, Michael Maynord, Chinmaya Devaraj, Cornelia Fermüller, Yiannis Aloimonos
Introduces Egocentric Object Manipulation Graphs (Ego-OMG):a novel representation for activity modeling and anticipation of near future actions.
Anton Mitrokhin, Peter Sutor, Douglas Summers-Stay, Cornelia Fermüller, Yiannis Aloimonos
By using hashing neural networks to produce binary vector representations of images, the authors show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output.
Frontiers in Robotics and AI
Anton Mitrokhin, Zhiyuan Hua, Cornelia Fermüller, Yiannis Aloimonos
Presents a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. Describes spatial and temporal features of event clouds, which provide cues for motion tracking and segmentation.
Computer Vision Foundation
Chethan M. Parameshwara, Nitin J. Sanket, Arjun Gupta, Cornelia Fermüller, Yiannis Aloimonos
A solution to multi-object motion segmentation using a combination of classical optimization methods along with deep learning and does not require prior knowledge of the 3D motion and the number and structure of objects.
John Kanu, Eadom Dessalene, Xiaomin Lin, Cornelia Fermüller, Yiannis Aloimonos
A novel robotic agent framework for learning to perform temporally extended tasks using spatial reasoning in a deep reinforcement learning framework, by sequentially imagining visual goals and choosing appropriate actions to fulfill imagined goals.
Aneesh Raghavan, John Baras
This paper pertains to stochastic multi-agent decision-making problems. The authors revisit the concepts of event-state-operation-structure and relationship of incompatibility from literature, and use them as a tool to study the algebraic structure of a set of events. They consider a multi-agent hypothesis testing problem and show that the set of events forms an ortholattice. They then consider the binary hypothesis testing problem wth finite observation space.
Aneesh Raghavan, John Baras
This paper pertains to hypothesis testing problems, specifically the problem of collaborative binary hypothesis testing.
Ion Matei, Johan de Kleer, Christoforos Somarakis, Rahul Rai, John Baras
To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. In this paper the authors use model-based interpretability, where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed.
Mohammad Mamduhi, Karl Johansson, Ehsan Hashemi, John Baras
This paper proposes an event-triggered, add-on safety mechanism in a networked vehicular system that can adjust control parameters for timely braking while maintaining maneuverability.
Cheng Li, Min Tang, Ruofeng tong, Ming Cai, Jieyi Zhao, Dinesh Manocha
Cloth simulation is an active area of research in computer graphics, computer-aided design (CAD) and the fashion industry. Over the last few decades many methods have been proposed for solving the underlying dynamical system with robust collision handling. The paper presents a novel parallel algorithm for cloth simulation that exploits multiple GPUs for fast computation and the handling of very high resolution meshes. It is the first approach that can perform almost interactive complex cloth simulation with wrinkles, friction and folds on commodity workstations.
Feixiang Lu, Zongdai Liu, Xibin Song, Dingfu Zhou, Wei Li, Hui Miao, Miao Liao, Liangjun Zhang, BinZhou, Ruigang Yang, Dinesh Manocha
The paper presents a robust and effective approach to reconstruct complete 3D poses and shapes of vehicles from a single image. It introduces a novel part-level representation for vehicle segmentation and 3D reconstruction, which significantly improves performance.
Andrew Best, Sahil Narang, Dinesh Manocha
Sense-Plan-act (SPA) is a new approach for generating plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. It extends prior work in propositional planning and natural language processing to enable agents to plan with uncertain information, and leverage question and answer dialogue with other agents and avatars to obtain the needed information and complete their goals. The agents are additionally able to respond to questions from the avatars and other agents using natural-language enabling real-time multi-agent multi-avatar communication environments.
Rohan Chandra, Uttaran Bhattacharya, Tanmay Randhavane, Aniket Bera, Dinesh Manocha
RoadTrack is a realtime tracking algorithm for autonomous driving that tracks heterogeneous road-agents in dense traffic videos. The approach is designed for dense traffic scenarios that consist of different road-agents such as pedestrians, two-wheelers, cars, buses, etc. sharing the road.
Zhiming Hu, Sheng Li, Congyi Zhang, Kangrui Yi, Guoping Wang, Dinesh Manocha
DGaze is a CNN-based model that combines object position sequence, head velocity sequence, and saliency features to predict users' gaze positions in HMD-based applications. The model can be applied to predict not only real-time gaze positions but also gaze positions in the near future and can achieve better performance than prior method.
IEEE Transactions on Visualization and Computer Graphics
Srihari Pratapa, Dinesh Manocha
RANDM is a random-access depth map compression algorithm for interactive rendering. The compressed representation provides random access to the depth values and enables real-time parallel decompression on commodity hardware. This method partitions the depth range captured in a given scene into equal-sized intervals and uses this partition to generate three separate components that exhibit higher coherence. Each of these components is processed independently to generate the compressed stream.
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Aniket Bera, Dinesh Manocha
CMetric classifies driver behaviors using centrality functions. The formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. This approach is designed for real-time autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video.
Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
The paper presents a learning-based method for detecting fake videos. The authors use the similarity between audio-visual modalities and the similarity between the affective cues of the two modalities to infer whether a video is “real” or “fake.”
Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
EmotiCon is a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. It uses multiple modalities of faces and gaits, background visual information and socio-dynamic inter-agent interactions to infer the perceived emotion. EmotiCon outperforms prior context-aware emotion recognition methods.
Abhishek Kumar, Trisha Mittal, Dinesh Manocha
MCQA is a learning-based algorithm for multimodal question answering that explicitly fuses and aligns the multi-modal input (i.e. text, audio, and video) forming the context for the query (question and answer).
Zhenyu Tang, Dinesh Manocha
Modern computer graphics applications including virtual reality and augmented reality have adopted techniques for both visual rendering and audio rendering. While visual rendering can already synthesize virtual objects into the real world seamlessly, it remains difficult to correctly blend virtual sound with real-world sound using state-of-the-art audio rendering. When the virtual sound is generated unaware of the scene, the corresponding application becomes less immersive, especially for AR. The authors present their current work on generating scene-aware sound using ray-tracing based simulation combined with deep learning and optimization.
2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops
Micah Taylor, Anish Chandak, Lakulish Antani, Dinesh Manocha
An algorithm and system for sound propagation and rendering in virtual environments and media applications. The approach uses geometric propagation techniques for fast computation of propagation paths from a source to a listener and takes into account specular reflections, diffuse reflections, and edge diffraction.
SriSai Naga Jyotish Poonganam, Bharath Gopalakrishnan, Venkata Seetharama Sai Bhargav Kumar Avula, K. Madhava Krishna, Arun Kumar Singh, Dinesh Manocha
A new model predictive control framework that improves reactive navigation for autonomous robots. The framework allows roboticists to compute low cost control inputs while ensuring some upper bound on the risk of collision.
IEEE Robotics and Automation Letters
Dinesh Manocha, Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Tanmay Randhavane
The authors' RoadTrack algorithm could help autonomous vehicles navigate dense traffic scenarios. The algorithm uses tracking-by-detection approach to detect vehicles and pedestrians, then predict where they are going.
Kurt Gray, Tanmay Randhavane, Kyra Kapsaskis, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures.
Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning.
Qiaoyun Wu, Dinesh Manocha, Jun Wang, Kai Xu
The authors improve the cross-target and cross-scene generalization of visual navigation through a learning agent guided by conceiving the next observations it expects to see. A variational Bayesian model, NeoNav, generates the next expected observations (NEO) conditioned on the current observations of the agent and the target view.
Uttaran Bhattacharya, Christian Roncal, Trisha Mittal, Rohan Chandra,Aniket Bera, Dinesh Manocha
The paper presents an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or from motion-captured data and represented as sequences of 3D poses.
Chaochao Li, Pei Lv, Mingliang Xu, Xinyu Wang, Dinesh Manocha, Bing Zhou, Meng Wang
In many applications such as human-robot interaction, autonomous driving or surveillance, it is important to accurately predict pedestrian trajectories for collision-free navigation or abnormal behavior detection. The authors present a novel trajectory prediction algorithm for pedestrians based on a personality-aware probabilistic feature map.
Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane (UNC), Aniket Bera, and Dinesh Manocha
STEP is a novel classifier network able to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network architecture. Given an RGB video of an individual walking, STEP implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. | Watch a video about STEP |
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha
The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles. | Watch a video about GraphRQI |
Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha
A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |
Emre Ozfatura, Sennur Ulukus, Deniz Gunduz
Introduces a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both computation time and decoding complexity by allowing a trade-off between the accuracy and the speed of computation. The approach is extended to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated.
2019 IEEE International Conference on Acoustics, Speech and Signal Processin; arXiv.org
Deniz Gunduz, Emre Ozfatura, Sennur Ulukus, Baturalp Buyukates
The age of information (AoI) metric is used to track the recovery frequency of partial computations in distributed gradient descent, the most common approach in supervised machine learning, a new solution to the problem of “straggling” worker machines.
Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus
Presents over-the-air adversarial attacks against deep learning-based modulation classifiers, accounting for realistic channel and broadcast transmission effects. A certified defense method using randomized smoothing is also included.