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Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
TL;DR: This article proposed an adaptive attention model with a visual sentinel to decide whether to attend to the image and where, in order to extract meaningful information for sequential word generation, which set the new state-of-the-art by a significant margin.
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Abstract: Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as "the" and "of". Other words that may seem visual can often be predicted reliably just from the language model e.g., "sign" after "behind a red stop" or "phone" following "talking on a cell". In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin.
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Citations
Learning to Collocate Neural Modules for Image Captioning
Xu Yang,Hanwang Zhang,Jianfei Cai +2 more
- 01 Oct 2019
TL;DR: Zhang et al. as discussed by the authors proposed learning to locate neural modules to generate the ''inner pattern'' connecting visual encoder and language decoder, which achieved state-of-the-art image captioning performance.
•Posted Content
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
TL;DR: A novel method of dynamically fuse multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities is proposed, which can robustly capture the high-level interactions between language and vision domains.
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•Posted Content
ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
TL;DR: Zhang et al. as discussed by the authors proposed an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image, which consists of multiple progressive steps and a discriminator is designed to classify whether the output image in the last progressive step is real or fake.
Topic-Oriented Image Captioning Based on Order-Embedding
TL;DR: Experiments on the image captioning task on the MS-COCO and Flickr30K datasets validate the usefulness of this framework by showing that the different given topics can lead to different captions describing specific aspects of the given image and that the quality of generated captions is higher than the control model without a topic as input.
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SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection
TL;DR: A novel 3D object detection network called SARPNET is introduced, which deploys a new low-level feature encoder to remedy the sparsity and inhomogeneity of LiDAR point clouds with an even sample method, and embodies a shape attention mechanism that learns the statistic 3D shape priors of objects and uses them to spatially enhance semantic embeddings.
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References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
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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.
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
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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.
Bleu: a Method for Automatic Evaluation of Machine Translation
Kishore Papineni,Salim Roukos,Todd Ward,Wei-Jing Zhu +3 more
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TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
- 01 Jan 2014
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
•Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau,Kyunghyun Cho,Yoshua Bengio +2 more
- 01 Jan 2015
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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