3 citations · 6 across the 3 of their papers we have counts for
10 papers
All You May Need for VQA are Image Captions
Soravit Changpinyo, Doron Kukliansky, Idan Szpektor +3
Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we…
2.5D Visual Relationship Detection
Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen +8
Visual 2.5D perception involves understanding the semantics and geometry of a scene through reasoning about object relationships with respect to the viewer in an environment. Howev…
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding +1
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. Howev…
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Cheng Zhang, Tai-Yu Pan, Yandong Li +5
Many objects do not appear frequently enough in complex scenes (e.g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by…
Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval
Soravit Changpinyo, Jordi Pont-Tuset, Vittorio Ferrari +1
Most existing image retrieval systems use text queries as a way for the user to express what they are looking for. However, fine-grained image retrieval often requires the ability…
Connecting Vision and Language with Localized Narratives
Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo +2
We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneo…