310 citations · 1.2k across the 64 of their papers we have counts for
7 papers · 1 filter
Light Field Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view…
Layered Controllable Video Generation
Jiahui Huang, Yuhe Jin, Kwang Moo Yi +1
We introduce layered controllable video generation, where we, without any supervision, decompose the initial frame of a video into foreground and background layers, with which the…
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…
Weakly-supervised Audio-visual Sound Source Detection and Separation
Tanzila Rahman, Leonid Sigal
Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from arti…
Energy-Based Learning for Scene Graph Generation
Mohammed Suhail, Abhay Mittal, Behjat Siddiquie +4
Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores…