73 citations · 189 across the 22 of their papers we have counts for
45 papers
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models
Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal +1
Neural architectures can be naturally viewed as computational graphs. Motivated by this perspective, we, in this paper, study neural architecture search (NAS) through the lens of l…
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
Sahithya Ravi, Aditya Chinchure, Leonid Sigal +2
There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus…
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
Peyman Bateni, Leonid Sigal
Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Bea…
TriBERT: Full-body Human-centric Audio-visual Representation Learning for Visual Sound Separation
Tanzila Rahman, Mengyu Yang, Leonid Sigal
The recent success of transformer models in language, such as BERT, has motivated the use of such architectures for multi-modal feature learning and tasks. However, most multi-moda…
Referring Transformer: A One-step Approach to Multi-task Visual Grounding
Muchen Li, Leonid Sigal
As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous appro…
Segmentation-grounded Scene Graph Generation
Siddhesh Khandelwal, Mohammed Suhail, Leonid Sigal
Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an…