Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Peter Anderson,Xiaodong He,Chris Buehler,Damien Teney,Mark Johnson,Stephen Gould,Lei Zhang +6 more
- pp 6077-6086
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TLDR
In this paper, a bottom-up and top-down attention mechanism was proposed to enable attention to be calculated at the level of objects and other salient image regions, which achieved state-of-the-art results on the MSCOCO test server.Citations
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Scene Graph Reasoning for Visual Question Answering.
TL;DR: This work proposes a novel method that approaches the visual question answering task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.
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A negative case analysis of visual grounding methods for VQA
TL;DR: This article proposed a simpler regularization scheme that does not require any external annotations and yet achieves near state-of-the-art performance on VQA-CPv2, which prevents overfitting to linguistic priors.
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A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Yikuan Li,Hanyin Wang,Yuan Luo +2 more
TL;DR: In this article, the authors adopt four pre-trained models: LXMERT, VisualBERT, UNIER and PixelBERT to learn multimodal representation from MIMIC-CXR images and associated reports.
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Long-Term Video Question Answering via Multimodal Hierarchical Memory Attentive Networks
TL;DR: Experimental results demonstrate that the proposed approach significantly outperforms other state-of-the-art methods for long-term videos answering, and extensive ablation studies are carried out to explore the reasons behind the proposed model’s effectiveness.
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Separating Skills and Concepts for Novel Visual Question Answering
TL;DR: The authors propose to separate skills and concepts within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts, which can be learned from unlabeled image-question pairs.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.