Program

Are we asking the right questions in MovieQA?.Bhavan Jasani (Robotics Institute, Carnegie Mellon University)*; Rohit Girdhar (Carnegie Mellon University); Deva Ramanan (Carnegie Mellon University) 

3. 

(Poster only) Video-Text Compliance: Activity Verification based on Natural Language Instructions,  Mayoore Jaiswal (IBM)*; Frank Liu (IBM Research); Anupama Jagannathan (IBM); Anne Gattiker (IBM); Inseok Hwang (IBM); Jinho Lee (Yonsei University); Matt Tong (IBm); Sahil Dureja (IBM); Soham Shah (IBM); Peter Hofstee (IBM); Valerie Chen (Yale University); Suvadip Paul (Stanford University); Rogerio Feris (IBM Research AI, MIT-IBM Watson AI Lab) 

 4. 

SUN-Spot: An RGB-D Dataset With Spatial Referring Expressions,  Cecilia Mauceri (University of Colorado Boulder)*; Christoffer Heckman (University of Colorado); Martha S Palmer (University of Colorado), supp 

 5. 

Evaluating Text-to-Image Matching using Binary Image Selection (BISON),  Hexiang Hu (USC)*; Ishan Misra (Facebook AI Research ); Laurens van der Maaten (Facebook), supp 

 13. 

Visual Storytelling via Predicting Anchor Word Embeddings in the Stories, Bowen Zhang (University of Southern California)*; Hexiang Hu (USC); Fei Sha (Google Research) 

 14. 

Prose for a Painting,  Prerna Kashyap (Columbia University)*; Samrat H Phatale (Columbia University); Iddo Drori (Columbia University and Cornell)

 16. 

Why Does a Visual Question Have Different Answers?,  Danna Gurari (University of Texas at Austin)*

 17. 

Analysis of diversity-accuracy tradeoff in image captioning, Ruotian Luo (Toyota Technological Institute at Chicago)*; Greg Shakhnarovich (TTI-Chicago)

 19. 

nocaps: novel object captioning at scale, Harsh Agrawal (Georgia Institute of Technology)*; Karan Desai (University of Michigan); Yufei Wang (Macquarie University); Xinlei Chen (Facebook AI Research); Rishabh Jain (Georgia Tech); Mark Johnson (Macquarie University); Dhruv Batra (Georgia Tech & Facebook AI Research); Devi Parikh (Georgia Tech & Facebook AI Research); Stefan Lee (Oregon State University); Peter Anderson (Georgia Tech)

20. 

Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach, Dong-Jin Kim (KAIST)*; Jinsoo Choi (KAIST); Tae-Hyun Oh (MIT CSAIL); In So Kweon (KAIST) 

21.

Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering, Soravit Changpinyo (Google AI)*; Bo Pang (); Piyush Sharma (Google Research); Radu Soricut (Google)

 Spotlight Presentation 2

22. 

MULE: Multimodal Universal Language Embedding,  Donghyun Kim (Boston University)*; Kuniaki Saito (Boston University); Kate Saenko (Boston University); Stan Sclaroff (Boston University); Bryan Plummer (Boston University)

 23.  

Incorporating 3D Information into Visual Question Answering, Yue Qiu (National Institute of Advanced Industrial Science and Technology (AIST),University of Tsukuba)*; Yutaka Satoh (National Institute of Advanced Industrial Science and Technology (AIST)); Kazuma Asano (National Institute of Advanced Industrial Science and Technology (AIST); University of Tsukuba); Kenji Iwata (National Institute of Advanced Industrial Science and Technology (AIST)); Ryota Suzuki (National Institute of Advanced Industrial Science and Technology (AIST)); Hirokatsu Kataoka (National Institute of Advanced Industrial Science and Technology (AIST))

 24. 

Multimodal Differential Network for Visual Question Generation, Badri Patro (IIT Kanpur)*; Sandeep Kumar (IIT Kanpur); Vinod Kumar Kurmi (IIT Kanpur); Vinay P Namboodiri (IIT Kanpur)

 25. 

Learning Semantic Sentence Embeddings using Pair-wise Discriminator, Badri Patro (IIT Kanpur)*; Vinod Kumar Kurmi (IIT Kanpur); Sandeep Kumar (IIT Kanpur); Vinay P Namboodiri (IIT Kanpur)

 26. 

Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning, Jyoti Aneja (University of Illinois, Urbana-Champaign)*; Harsh Agrawal (Georgia Institute of Technology)

 27. 

Reinforcing an Image Caption Generator using Off-line Human Feedback, Paul Hongsuck Seo (POSTECH)*; Piyush Sharma (Google Research); Tomer Levinboim (Google); Bohyung Han (Seoul National University); Radu Soricut (Google)

 28. 

Use What You Have: Video retrieval using representations from collaborative experts, Yang Liu (University of Oxford)*; Samuel Albanie (University of Oxford); Arsha Nagrani (Oxford University ); Andrew Zisserman (University of Oxford)

 29. 

CDAR 2019 Competition on Scene Text Visual Question Answering, Ali Furkan Biten (Computer Vision Center); Rubèn Tito (Computer Vision Center); Andrés Mafla (Computer Vision Centre); Lluis Gomez (Universitat Autónoma de Barcelona)*; Marçal Rusiñol (Computer Vision Center, UAB); Minesh Mathew (CVIT, IIIT-Hyderabad); C.V. Jawahar (IIIT-Hyderabad); Ernest Valveny (Universitat Autónoma de Barcelona); Dimosthenis Karatzas (Computer Vision Centre)

 30. 

Recognizing and Characterizing Natural Language Descriptions of Visually Complex Images,  Ziyan Yang (University of Virginia)*; Yangfeng Ji (University of Virginia); Vicente Ordonez (University of Virginia) 

 31. 

Adversarial Learning of Semantic Relevance in Text to Image Synthesis,  Miriam Cha (Harvard University)*; Youngjune Gwon (Samsung SDS); H.T. Kung (Harvard University) 

 32. 

 (Poster only)  ShapeGlot: Learning Language for Shape Differentiation, Panos Achlioptas, Judy Fan, Robert Hawkins, Noah Goodman, Leonidas Guibas Conference Paper International Conference on Computer Vision, 2019, Seoul

VATEX Challenge Presentations 

Multi-modal Information Fusion and Multi-stage Training Strategy 

for Video Captioning

Ziqi Zhang*,Yaya Shi*, Jiutong Wei*,Chunfeng Yuan, Bing Li, Weiming Hu 

 Integrating Temporal and Spatial Attentions for VATEX Video 

Captioning Challenge 2019 

Shizhe Chen, Yida Zhao, Yuqing Song, Qin Jin, Qi Wu

Multi-View Features and Hybrid Reward Strategies for VATEX Video Captioning Challenge 2019

Xinxin Zhu*, Longteng Guo*, Peng Yao*, Jing Liu, Hanqing Lu