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Towards VQA Models That Can Read
Amanpreet Singh, Vivek Natarajan, Meet Shah +5
Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models ca…
Multi-Target Embodied Question Answering
Licheng Yu, Xinlei Chen, Georgia Gkioxari +3
Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA makes the fundamental a…
Embodied Visual Recognition
Jianwei Yang, Zhile Ren, Mingze Xu +4
Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to…
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai +5
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from…
TensorMask: A Foundation for Dense Object Segmentation
Xinlei Chen, Ross Girshick, Kaiming He +1
Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segm…
Cycle-Consistency for Robust Visual Question Answering
Meet Shah, Xinlei Chen, Marcus Rohrbach +1
Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired. We introduce a new evaluation protocol and asso…