Watch What You Just Said: Image Captioning with Text-Conditional Attention

By Luowei Zhou, Chenliang Xu, Parker A. Koch, Jason J. Corso. 2016

85 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Multimodal Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Yi Yang
4. Cordelia Schmid
5. Trevor Darrell
6. Tao Mei
7. Xiaogang Wang
8. Andrew Zisserman

In Multimodal Learning

1. Tao Mei
2. Ting Yao
3. Yingwei Pan
4. Yehao Li
5. Wei David Liu
6. Marcus Rohrbach
7. Luowei Zhou
8. Lin Ma

Citations in the corpus

• Towards Automatic Learning of Procedures From Web Instructional Videos (2017, methodology): “Then, we extract the frame-wise ResNet-34 feature (He et al. 2016), 3 pretrained on both ImageNet (Deng et al. 2009) and MSCOCO caption (Lin et al. 2014; Zhou et al. 2016).”
• End-to-End Dense Video Captioning with Masked Transformer (2018, neutral): “In contrast to earlier video captioning papers, which are based on models like hidden Markov models and ontologies [39, 6], recent work on captioning is dominated by deep neural network-based methods [32, 34, 37, 43, 36, 26].”
• Grounded Video Description (2018, background): “Visual attention usually comes in the form of temporal attention [34] (or spatial-attention [32] in the image domain), semantic attention [13, 35, 36, 41] or both [19].”
• Boosting Image Captioning with Attributes (2016, methodology): “As the annotations of the official testing set are not publicly available, we follow the widely used settings in prior works (You et al., 2016; Zhou et al., 2016) and take 82,783 images for training, 5,000 for validation and 5,000 for testing.”
• EEGtoText: Learning to Write Medical Reports from EEG Recordings (2019, background): “Caption Generation There has been a large body of works that build connection between visual data (e.g., images or videos) and textual data (e.g., summary or caption) in computer vision domain(Vinyals et al., 2015; Xu et al., 2015b; Rennie et al., 2017; Zhou et al., 2017).”
• Recurrent Fusion Network for Image Captioning (2018, methodology): “We compare our proposed RFNet with the state-of-the-art approaches on image captioning, including Neural Image Caption (NIC) [18], Attribute LSTM [22], LSTM-A3 [23], Recurrent Image Captioner (RIC) [45], Recurrent Highway Network (RHN)[46], Soft Attention model [12], Attribute Attention model [48], ”
• SibNet: Sibling Convolutional Encoder for Video Captioning (2018, background): “The simple encoderdecoder pipeline has achieved exceptional performance in describing visual contents with natural language [5]–[8], [10], [13]–[15].”