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
Reads0
Chats0
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.Abstract:
Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.read more
Citations
More filters
Book ChapterDOI
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
Keren Ye,Adriana Kovashka +1 more
TL;DR: In this paper, the authors use symbolic references to better understand the meaning of an ad and further show how anchoring ad understanding in general-purpose object recognition and image captioning improves results.
Proceedings ArticleDOI
Vision-Dialog Navigation by Exploring Cross-Modal Memory
TL;DR: The Cross-modal Memory Network (CMN) is proposed for remembering and understanding the rich information relevant to historical navigation actions and outperforms the previous state-of-the-art model on both seen and unseen environments.
Proceedings ArticleDOI
Multi-Level Visual-Semantic Alignments with Relation-Wise Dual Attention Network for Image and Text Matching.
TL;DR: A relation-wise dual attention network (RDAN) for image-text matching is proposed, which maintains an over-complete set that contains pairs of regions and words and encodes the local correlations and the global dependencies between regions and Words by training a visual-semantic network.
Proceedings ArticleDOI
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
TL;DR: A Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an emotion polarity constraint and can generate a polarity preserved attention map and thus improve the emotion regression performance.
Proceedings ArticleDOI
Faithful Multimodal Explanation for Visual Question Answering
Jialin Wu,Raymond J. Mooney +1 more
TL;DR: This article proposed a novel approach to develop a highperforming VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations.
References
More filters
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.