Vision-and-Language (V&L) Task
(Redirected from Visual Linguistic Task)
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A Vision-and-Language (V&L) Task is a vision task that is a linguistic task.
- Example(s):
- See: Vision-and-Language (V&L) Model.
References
2021
- (Shen et al., 2021) ⇒ Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer. (2021). “How Much Can CLIP Benefit Vision-and-Language Tasks?. ” arXiv preprint arXiv:2107.06383
- ABSTRACT: Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks. We release our code at this https URL.
- QUOTE: Vision-and-Language (V&L) models. V&L tasks require a model to understand the visual world and to ground natural language to the visual observations. Prominent tasks include visual question answering (Antol et al., 2015), image captioning (Chen et al., 2015), vision-language navigation (Anderson et al., 2018a), image-text retrieval (Wang et al., 2016) and so on. V&L models designed for these tasks often consist of a visual encoder, a text encoder, and a cross-modal interaction module (Kim et al., 2021).
We illustrate the three typical training stages in Figure 1: 1) the visual encoder is trained on annotated vision datasets (Russakovsky et al., 2015; Krishna et al., 2017) (denoted as visual encoder pre-training); 2) (optionally) pre-training on paired image-caption data with a reconstructive objective and an image-text matching objective (denoted as vision-and-language pre-training) (Lu et al., 2019); 3) fine-tuning on task-specific data (denoted as taskspecific fine-tuning).