12 citations · 21 across the 7 of their papers we have counts for
8 papers
Unsupervised Scene Sketch to Photo Synthesis
Jiayun Wang, Sangryul Jeon, Stella X. Yu +3
Sketches make an intuitive and powerful visual expression as they are fast executed freehand drawings. We present a method for synthesizing realistic photos from scene sketches. Wi…
Structured Graph Variational Autoencoders for Indoor Furniture layout Generation
Aditya Chattopadhyay, Xi Zhang, David Paul Wipf +2
We present a structured graph variational autoencoder for generating the layout of indoor 3D scenes. Given the room type (e.g., living room or library) and the room layout (e.g., r…
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network
Vibhav Agarwal, Sudeep Deepak Shivnikar, Sourav Ghosh +2
Intent detection is a crucial task in any Natural Language Understanding (NLU) system and forms the foundation of a task-oriented dialogue system. To build high-quality real-world…
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
Jasmine Collins, Shubham Goel, Kenan Deng +9
We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metad…
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View
Xi Zhang, Chun-Kai Wang, Kenan Deng +2
In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they re…
Multimodal Shape Completion via IMLE
Himanshu Arora, Saurabh Mishra, Shichong Peng +2
Shape completion is the problem of completing partial input shapes such as partial scans. This problem finds important applications in computer vision and robotics due to issues su…
A character representation enhanced on-device Intent Classification
Sudeep Deepak Shivnikar, Himanshu Arora, Harichandana B S S
Intent classification is an important task in natural language understanding systems. Existing approaches have achieved perfect scores on the benchmark datasets. However they are n…
Contextual Diversity for Active Learning
Sharat Agarwal, Himanshu Arora, Saket Anand +1
Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active…