44 citations · 149 across the 8 of their papers we have counts for
7 papers · 1 filter
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
Yi Zhou, Shuyang Sun, Chao Zhang +2
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem. Under this formulation, predicate categories are…
Disentangling Pose from Appearance in Monochrome Hand Images
Yikang Li, Chris Twigg, Yuting Ye +2
Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background. While some success has been achieved using deep neural…
Question-Guided Hybrid Convolution for Visual Question Answering
Peng Gao, Pan Lu, Hongsheng Li +4
In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textu…
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Yikang Li, Wanli Ouyang, Bolei Zhou +3
Generating scene graph to describe all the relations inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with s…
Visual Question Generation as Dual Task of Visual Question Answering
Yikang Li, Nan Duan, Bolei Zhou +3
Recently visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, which have been explored separately. In this work, we…
Scene Graph Generation from Objects, Phrases and Region Captions
Yikang Li, Wanli Ouyang, Bolei Zhou +2
Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generate…
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
Yikang Li, Wanli Ouyang, Xiaogang Wang +1
As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive po…