310 citations · 1.2k across the 64 of their papers we have counts for
8 papers · 1 filter
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…
Make-A-Story: Visual Memory Conditioned Consistent Story Generation
Tanzila Rahman, Hsin-Ying Lee, Jian Ren +3
There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches re…
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…
Generalizable Patch-Based Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The…
Iterative Scene Graph Generation
Siddhesh Khandelwal, Leonid Sigal
The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large…
Self-supervision through Random Segments with Autoregressive Coding (RandSAC)
Tianyu Hua, Yonglong Tian, Sucheng Ren +3
Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with…
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Peyman Bateni, Jarred Barber, Raghav Goyal +4
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examp…