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"normalizedAbstract": "Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and little research has been done on explaining multimodal models. In this paper, we present an interactive visual analytics system, M2 Lens, to visualize and explain multimodal models for sentiment analysis. M2 Lens provides explanations on intra- and inter-modal interactions at the global, subset, and local levels. Specifically, it summarizes the influence of three typical interaction types (i.e., dominance, complement, and conflict) on the model predictions. Moreover, M2 Lens identifies frequent and influential multimodal features and supports the multi-faceted exploration of model behaviors from language, acoustic, and visual modalities. Through two case studies and expert interviews, we demonstrate our system can help users gain deep insights into the multimodal models for sentiment analysis.",
"title": "M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis",
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"abstract": "Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are collectively encoded by multiple neurons. We present Neurocartography, an interactive system that scalably summarizes and visualizes concepts learned by neural networks. It automatically discovers and groups neurons that detect the same concepts, and describes how such neuron groups interact to form higher-level concepts and the subsequent predictions. Neurocartography introduces two scalable summarization techniques: (1) neuron clustering groups neurons based on the semantic similarity of the concepts detected by neurons (e.g., neurons detecting “dog faces” of different breeds are grouped); and (2) neuron embedding encodes the associations between related concepts based on how often they co-occur (e.g., neurons detecting “dog face” and “dog tail” are placed closer in the embedding space). Key to our scalable techniques is the ability to efficiently compute all neuron pairs' relationships, in time linear to the number of neurons instead of quadratic time. Neurocartography scales to large data, such as the ImageNet dataset with 1.2M images. The system's tightly coordinated views integrate the scalable techniques to visualize the concepts and their relationships, projecting the concept associations to a 2D space in Neuron Projection View, and summarizing neuron clusters and their relationships in Graph View. Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts. And through usage scenarios, we describe how our approaches enable interesting and surprising discoveries, such as concept cascades of related and isolated concepts. The Neurocartography visualization runs in modern browsers and is open-sourced.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are collectively encoded by multiple neurons. We present Neurocartography, an interactive system that scalably summarizes and visualizes concepts learned by neural networks. It automatically discovers and groups neurons that detect the same concepts, and describes how such neuron groups interact to form higher-level concepts and the subsequent predictions. Neurocartography introduces two scalable summarization techniques: (1) neuron clustering groups neurons based on the semantic similarity of the concepts detected by neurons (e.g., neurons detecting “dog faces” of different breeds are grouped); and (2) neuron embedding encodes the associations between related concepts based on how often they co-occur (e.g., neurons detecting “dog face” and “dog tail” are placed closer in the embedding space). Key to our scalable techniques is the ability to efficiently compute all neuron pairs' relationships, in time linear to the number of neurons instead of quadratic time. Neurocartography scales to large data, such as the ImageNet dataset with 1.2M images. The system's tightly coordinated views integrate the scalable techniques to visualize the concepts and their relationships, projecting the concept associations to a 2D space in Neuron Projection View, and summarizing neuron clusters and their relationships in Graph View. Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts. And through usage scenarios, we describe how our approaches enable interesting and surprising discoveries, such as concept cascades of related and isolated concepts. The Neurocartography visualization runs in modern browsers and is open-sourced.",
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"normalizedAbstract": "Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are collectively encoded by multiple neurons. We present Neurocartography, an interactive system that scalably summarizes and visualizes concepts learned by neural networks. It automatically discovers and groups neurons that detect the same concepts, and describes how such neuron groups interact to form higher-level concepts and the subsequent predictions. Neurocartography introduces two scalable summarization techniques: (1) neuron clustering groups neurons based on the semantic similarity of the concepts detected by neurons (e.g., neurons detecting “dog faces” of different breeds are grouped); and (2) neuron embedding encodes the associations between related concepts based on how often they co-occur (e.g., neurons detecting “dog face” and “dog tail” are placed closer in the embedding space). Key to our scalable techniques is the ability to efficiently compute all neuron pairs' relationships, in time linear to the number of neurons instead of quadratic time. Neurocartography scales to large data, such as the ImageNet dataset with 1.2M images. The system's tightly coordinated views integrate the scalable techniques to visualize the concepts and their relationships, projecting the concept associations to a 2D space in Neuron Projection View, and summarizing neuron clusters and their relationships in Graph View. Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts. And through usage scenarios, we describe how our approaches enable interesting and surprising discoveries, such as concept cascades of related and isolated concepts. The Neurocartography visualization runs in modern browsers and is open-sourced.",
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"abstract": "Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level <italic>(what the constituents are)</italic> and clip-level <italic>(how those constituents are organized)</italic>. We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by <italic>selecting the data</italic> to visualize instead of manually <italic>drawing the graphical marks</italic>. Our system can be generalized to other racket sports <italic>(e.g</italic>., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.",
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"content": "Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level <italic>(what the constituents are)</italic> and clip-level <italic>(how those constituents are organized)</italic>. We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by <italic>selecting the data</italic> to visualize instead of manually <italic>drawing the graphical marks</italic>. Our system can be generalized to other racket sports <italic>(e.g</italic>., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.",
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"abstract": "Hand interaction techniques in virtual reality often exploit visual dominance over proprioception to remap physical hand movements onto different virtual movements. However, when the offset between virtual and physical hands increases, the remapped virtual hand movements are hardly self-attributed, and the users become aware of the remapping. Interestingly, the sense of self-attribution of a body is called the sense of body ownership (SoBO) in the field of psychology, and the realistic the avatar, the stronger is the SoBO. Hence, we hypothesized that realistic avatars (i.e., human hands) can foster self-attribution of the remapped movements better than abstract avatars (i.e., spherical pointers), thus making the remapping less noticeable. In this article, we present an experiment in which participants repeatedly executed reaching movements with their right hand while different amounts of horizontal shifts were applied. We measured the remapping detection thresholds for each combination of shift directions (left or right) and avatar appearances (realistic or abstract). The results show that realistic avatars increased the detection threshold (i.e., lowered sensitivity) by 31.3 percent than the abstract avatars when the leftward shift was applied (i.e., when the hand moved in the direction away from the body-midline). In addition, the proprioceptive drift (i.e., the displacement of self-localization toward an avatar) was larger with realistic avatars for leftward shifts, indicating that visual information was given greater preference during visuo-proprioceptive integration in realistic avatars. Our findings quantifiably show that realistic avatars can make remapping less noticeable for larger mismatches between virtual and physical movements and can potentially improve a wide variety of hand-remapping techniques without changing the mapping itself.",
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"content": "Hand interaction techniques in virtual reality often exploit visual dominance over proprioception to remap physical hand movements onto different virtual movements. However, when the offset between virtual and physical hands increases, the remapped virtual hand movements are hardly self-attributed, and the users become aware of the remapping. Interestingly, the sense of self-attribution of a body is called the sense of body ownership (SoBO) in the field of psychology, and the realistic the avatar, the stronger is the SoBO. Hence, we hypothesized that realistic avatars (i.e., human hands) can foster self-attribution of the remapped movements better than abstract avatars (i.e., spherical pointers), thus making the remapping less noticeable. In this article, we present an experiment in which participants repeatedly executed reaching movements with their right hand while different amounts of horizontal shifts were applied. We measured the remapping detection thresholds for each combination of shift directions (left or right) and avatar appearances (realistic or abstract). The results show that realistic avatars increased the detection threshold (i.e., lowered sensitivity) by 31.3 percent than the abstract avatars when the leftward shift was applied (i.e., when the hand moved in the direction away from the body-midline). In addition, the proprioceptive drift (i.e., the displacement of self-localization toward an avatar) was larger with realistic avatars for leftward shifts, indicating that visual information was given greater preference during visuo-proprioceptive integration in realistic avatars. Our findings quantifiably show that realistic avatars can make remapping less noticeable for larger mismatches between virtual and physical movements and can potentially improve a wide variety of hand-remapping techniques without changing the mapping itself.",
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