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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Answer Them All! Toward Universal Visual Question Answering Models
TL;DR: In this paper, the authors compare five state-of-the-art VQA algorithms across eight different datasets covering both natural image understanding and synthetic data sets and find that methods do not generalize across the two domains.
Book ChapterDOI
Learning to Generate Grounded Visual Captions Without Localization Supervision
TL;DR: This paper proposed a cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it, and then reconstruct the sentence from the localized image region(s) to match the ground-truth.
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Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps
TL;DR: This paper argues that a simple attention mechanism can do the same or even better job without any bells and whistles of multi-modality encoder design, and finds this simple baseline model consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of ST-V QA.
Proceedings ArticleDOI
Object-Difference Attention: A Simple Relational Attention for Visual Question Answering
TL;DR: An object-difference attention (ODA) is proposed which calculates the probability of attention by implementing difference operator between different image objects in an image under the guidance of questions in hand and Experimental results show those relational attentions have strengths on different types of questions.
Proceedings ArticleDOI
Cascade Reasoning Network for Text-based Visual Question Answering
TL;DR: A novel Cascade Reasoning Network (CRN) is proposed that consists of a progressive attention module (PAM) and a multimodal reasoning graph (MRG) module that aims to explicitly model the connections and interactions between texts and visual concepts.
References
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Proceedings ArticleDOI
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.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
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.