310 citations · 1.2k across the 64 of their papers we have counts for
9 papers · 1 filter
An Improved Attention for Visual Question Answering
Tanzila Rahman, Shih-Han Chou, Leonid Sigal +1
We consider the problem of Visual Question Answering (VQA). Given an image and a free-form, open-ended, question, expressed in natural language, the goal of VQA system is to provid…
Person-in-Context Synthesiswith Compositional Structural Space
Weidong Yin, Ziwei Liu, Leonid Sigal
Despite significant progress, controlled generation of complex images with interacting people remains difficult. Existing layout generation methods fall short of synthesizing reali…
Attribute-guided image generation from layout
Ke Ma, Bo Zhao, Leonid Sigal
Recent approaches have achieved great success in image generation from structured inputs, e.g., semantic segmentation, scene graph or layout. Although these methods allow specifica…
Unsupervised Video Decomposition using Spatio-temporal Iterative Inference
Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal +1
Unsupervised multi-object scene decomposition is a fast-emerging problem in representation learning. Despite significant progress in static scenes, such models are unable to levera…
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
Jing Wang, Jiahong Chen, Jianzhe Lin +2
In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize we…
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation
Siddhesh Khandelwal, Raghav Goyal, Leonid Sigal
Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate t…
Consistent Multiple Sequence Decoding
Bicheng Xu, Leonid Sigal
Sequence decoding is one of the core components of most visual-lingual models. However, typical neural decoders when faced with decoding multiple, possibly correlated, sequences of…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction
Yuan Yao, Nico Schertler, Enrique Rosales +3
Reconstruction of a 3D shape from a single 2D image is a classical computer vision problem, whose difficulty stems from the inherent ambiguity of recovering occluded or only partia…