Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information

Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This paper introduces an unsupervised VWSD approach that uses gloss information of an external lexical knowledge-base, especially the sense definitions. Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided. In addition, to ameliorate the out-of-dictionary (OOD) issue, we propose a context-aware definition generation with GPT-3. Experimental results show that the VWSD performance significantly increased with our Bayesian inference-based approach. In addition, our context-aware definition generation achieved prominent performance improvement in OOD examples exhibiting better performance than the existing definition generation method. We will publish source codes as soon as possible.Comment: ACL 202

Cross-lingual Visual Verb Sense Disambiguation

Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9,504 images annotated with English, German, and Spanish verbs. Each image in MultiSense is annotated with an English verb and its translation in German or Spanish. We show that cross-lingual verb sense disambiguation models benefit from visual context, compared to unimodal baselines. We also show that the verb sense predicted by our best disambiguation model can improve the results of a text-only machine translation system when used for a multimodal translation task.Comment: NAACL 2019; fix typo in author nam

Visual context for verb sense disambiguation and multilingual representation learning

Every day billions of images are uploaded to the web. To process images at such a large scale it is important to build automatic image understanding systems. An important step towards understanding the content of the images is to be able to understand all the objects, scenes and actions depicted in the image. These systems should be capable of integrating with natural language or text to be able to query and interact with humans for tasks such as image retrieval. Verbs play a key role in the understanding of sentences and scenes. Verbs express the semantics of an actions as well as the interactions between objects participating in an event. Thus understanding verbs is central to both language and image understanding. However, verbs are known for their variability in meaning with context. Many studies in psychology have shown that contextual information plays an important role in semantic understanding and processing in the human visual system. We use this as intuition and understand the role of textual or visual context in tasks that combine language and vision. Our research presented in this thesis focuses on the problems of integrating visual and textual contexts for: (i) automatically identifying verbs that denote actions depicted in the images; (ii) fine-grained analysis of how visual context can help disambiguate different meanings of verbs in a language or across languages; (iii) the role played by the visual and multilingual context in learning representations that allow us to query information across modalities and languages. First, we propose the task of visual sense disambiguation, an alternative way of addressing the action recognition task. Instead of identifying the actions directly, we develop a two step process: identifying the verb that denotes the action being depicted in an image and then disambiguate the meaning of the verb based on the visual and textual context associated with the image. We first build a image-verb classifier based on the weak signal from image description data and analyse the specific regions that model focuses on while predicting the verb. We then disambiguate the meaning of the verb shown in the image using image features and sense-inventories. We test the hypothesis that visual and textual context associated with the image contribute to the disambiguation task. Second, we ask whether the predictions made by such models correspond to human intuitions about visual verbs or actions. We analyse whether the image regions a verb prediction model identifies as salient for a given verb correlate with the regions fixated by human observers performing an action classification task. We also compare the correlation of human fixations against visual saliency and center bias models. Third, we propose the crosslingual verb disambiguation task: identifying the correct translation of the verb in a target language based on visual context. This task has the potential to resolve lexical ambiguity in machine translation when the visual context is available. We propose a series of models and show that multimodal models that fuse textual information with visual features have an edge over text or visual only models. We then demonstrate how visual sense disambiguation can be combined with lexical constraint decoding to improve the performance of a standard unimodal machine translation system on image descriptions. Finally, we move on to learn joint representations for images and text in multiple languages. We test the hypothesis that context provided as visual information or text in other language contributes to better representation learning. We propose models to map text from multiple languages and images into a common space and evaluating the usefulness of the second language in multimodal search and usefulness of image in the crosslingual search. Our experiments suggest that exploiting multilingual and multimodal resources can help in learning better semantic representations that are useful for various multimodal natural language understanding tasks. Our experiments on visual sense disambiguation, sense disambiguation across languages, multimodal and cross-lingual search demonstrate that visual context alone or combined with textual context is useful for enhancing multimodal and crosslingual applications

Learning Transferable Representations for Hierarchical Relationship Exploration

The visual scenes are composed of basic elements, such as objects, parts, and other semantic regions. It is well-acknowledged that humans perceive the world in a compositional and hierarchical way in which visual scenes are treated as a layout of distinct semantic objects/attributes/parts. Those separated objects/attributes/parts are linked together via different relationships, including visual relationships and semantic relationships. Particularly, the shared parts/attributes/objects of the visual concepts (object, visual relationships), are shared and thus transferable among different visual concepts. Humans can easily imagine a new composite concept from the shared parts of different concepts, while one of the important shortcomings of current deep neural networks is the compositional perception ability and thus it requires a large scale of data to optimize the deep neural networks. From the perspective of compositional perception, this thesis thinks one of the limitations of typical neural networks is that the factor representations of deep neural networks are not sharable and transferable among different concepts. Therefore, the thesis introduces various techniques, including compositional learning framework, compositional invariant learning, and BatchFormer module, to enable the factor representations of deep neural networks sharable and transferable among different concepts for hierarchical relationship exploration, involving human-object interaction, 3D human-object interaction and sample relationships