53 citations · 185 across the 13 of their papers we have counts for
15 papers
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith +5
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects…
(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering
Anoop Cherian, Chiori Hori, Tim K. Marks +1
Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approache…
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Safa C. Medin, Bernhard Egger, Anoop Cherian +4
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate striking…
Audio-Visual Scene-Aware Dialog and Reasoning using Audio-Visual Transformers with Joint Student-Teacher Learning
Ankit P. Shah, Shijie Geng, Peng Gao +5
In previous work, we have proposed the Audio-Visual Scene-Aware Dialog (AVSD) task, collected an AVSD dataset, developed AVSD technologies, and hosted an AVSD challenge track at bo…
InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images
Anoop Cherian, Goncalo Dias Pais, Siddarth Jain +2
In this paper, we present InSeGAN, an unsupervised 3D generative adversarial network (GAN) for segmenting (nearly) identical instances of rigid objects in depth images. Using an an…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…