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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Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions
TL;DR: Zhang et al. as mentioned in this paper proposed a simple yet effective layer architecture of neural networks, which performs multiple operations in parallel, which are weighted by an attention mechanism to enable selection of proper operations depending on the input.
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Multi-modality Latent Interaction Network for Visual Question Answering
TL;DR: The proposed Multi- modality Latent Interaction module (MLI) learns the cross-modality relationships between latent visual and language summarizations, which summarize visual regions and question into a small number of latent representations to avoid modeling uninformative individual region-word relations.
Journal ArticleDOI
S2DNet: Depth Estimation From Single Image and Sparse Samples
TL;DR: The experimental analysis shows that the proposed S2DNet outperforms the existing state-of-the-art methods for both single image depth estimation and image de-hazing.
Journal ArticleDOI
Multimodal research in vision and language: A review of current and emerging trends
Shagun Uppal,Sarthak Bhagat,Devamanyu Hazarika,Navonil Majumder,Soujanya Poria,Roger Zimmermann,Amir Zadeh +6 more
TL;DR: A detailed overview of the latest trends in research pertaining to visual and language modalities is presented, looking at its applications in their task formulations and how to solve various problems related to semantic perception and content generation.
Journal ArticleDOI
Show, Tell, and Polish: Ruminant Decoding for Image Captioning
TL;DR: Experiments on two datasets demonstrate that the ruminant decoding method can bring significant improvements over traditional single-pass decoding based models and achieves state-of-the-art performance.
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.