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Exploring the Challenges towards Lifelong Fact Learning
Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi +3
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the propos…
Grounded Video Description
Luowei Zhou, Yannis Kalantidis, Xinlei Chen +2
Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typi…
Adversarial Inference for Multi-Sentence Video Description
Jae Sung Park, Marcus Rohrbach, Trevor Darrell +1
While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence…
Graph-Based Global Reasoning Networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan +3
Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) exce…
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
Satwik Kottur, José M. F. Moura, Devi Parikh +2
Visual dialog entails answering a series of questions grounded in an image, using dialog history as context. In addition to the challenges found in visual question answering (VQA),…
Pythia v0.1: the Winning Entry to the VQA Challenge 2018
Yu Jiang, Vivek Natarajan, Xinlei Chen +3
This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. Our starting point is a modular re-implementation o…