Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

EMNLP 2016 Akira FukuiDong Huk ParkDaylen YangAnna RohrbachTrevor DarrellMarcus Rohrbach

Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks such as visual question answering require combining these vector representations with each other... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Visual Question Answering COCO Visual Question Answering (VQA) real images 1.0 multiple choice MCB 7 att. Percentage correct 70.1 # 1
Visual Question Answering COCO Visual Question Answering (VQA) real images 1.0 open ended MCB 7 att. Percentage correct 66.5 # 1
Phrase Grounding Flickr30k Entities Test MCB Accuracy 48.69 # 1
Phrase Grounding ReferIt MCB Accuracy 28.91 # 1
Visual Question Answering Visual7W MCB+Att. Percentage correct 62.2 # 3
Visual Question Answering VQA v1 test-dev MCB (ResNet) Accuracy 64.2 # 3
Visual Question Answering VQA v2 test-dev MCB Accuracy 64.7 # 17