Motion-Aware Memory Network for Fast Video Salient Object Detection.

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, these methods still suffer from high computational costs or poor quality of the generated saliency maps. To address this, we design a space-time memory (STM)-based network that employs a standard encoder-decoder architecture. During the encoding stage, we extract high-level temporal features from the current frame and its adjacent frames, which is more efficient and practical than methods reliant on optical flow. During the decoding stage, we introduce an effective fusion strategy for both spatial and temporal branches. The semantic information of the high-level features is used to improve the object details in the low-level features. Subsequently, spatiotemporal features are methodically derived step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision prevalent in image salient object detection (ISOD), we design a motion-aware loss that predicts object boundary motion, and simultaneously perform multitask learning for VSOD and object motion prediction. This can further enhance the model's capability to accurately extract spatiotemporal features while maintaining object integrity. Extensive experiments on several datasets demonstrate the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. Our proposed model does not require optical flow or additional preprocessing, and can reach an impressive inference speed of nearly 100 FPS.

Rigel: Transforming Tabular Data by Declarative Mapping.

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness.

MUSE: Visual Analysis of Musical Semantic Sequence.

Visualization has the capacity of converting auditory perceptions of music into visual perceptions, which consequently opens the door to music visualization (e.g., exploring group style transitions and analyzing performance details). Current research either focuses on low-level analysis without constructing and comparing music group characteristics, or concentrates on high-level group analysis without analyzing and exploring detailed information. To fill this gap, integrating the high-level group analysis and low-level details exploration of music, we design a musical semantic sequence visualization analytics prototype system (MUSE) that mainly combines a distribution view and a semantic detail view, assisting analysts in obtaining the group characteristics and detailed interpretation. In the MUSE, we decompose the music into note sequences for modeling and abstracting music into three progressively fine-grained pieces of information (i.e., genres, instruments and notes). The distribution view integrates a new density contour, which considers sequence distance and semantic similarity, and helps analysts quickly identify the distribution features of the music group. The semantic detail view displays the music note sequences and combines the window moving to avoid visual clutter while ensuring the presentation of complete semantic details. To prove the usefulness and effectiveness of MUSE, we perform two case studies based on real-world music MIDI data. In addition, we conduct a quantitative user study and an expert evaluation.

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

DT2VIS: A Focus+Context Answer Generation System to Facilitate Visual Exploration of Tabular Data.

The visual analysis dialog system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intention of a user but not on generating answers and inducing explorations. A focus+context answer generation approach, which allows users to obtain insight and contextual information simultaneously, is proposed in this work to address the incomplete user query (i.e., input query cannot reflect all possible intentions of the user). A query recommendation algorithm, which applies the historical query information of a user to recommend a follow-up query, is also designed and implemented to provide an in-depth exploration. These ideas are implemented in a system called DT2VIS. Specific cases of utilizing DT2VIS are also provided to analyze data. Finally, the results show that DT2VIS could help users easily and efficiently reach their analysis goals in a comparative study.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams.

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.

Interplay between Public Attention and Public Emotion toward Multiple Social Issues on Twitter.

This study aims to elucidate the intricate interplay between public attention and public emotion toward multiple social issues. A theoretical framework is developed based on three perspectives including endogenous affect hypothesis, affect transfer hypothesis, and affective intelligence theory. Large-scale longitudinal data with 265 million tweets on five social issues are analyzed using a time series analytical approach. Public attention on social issues can influence public emotion on the issue per se. Social issues interact with one another to attract public attention in both cooperative and competitive ways. Instead of a direct transfer from public emotion to public attention, the public emotion toward a social issue moderates the interaction between the issue and other issue(s).

Embedding Spatio-Temporal Information into Maps by Route-Zooming.

Analysis and exploration of spatio-temporal data such as traffic flow and vehicle trajectories have become important in urban planning and management. In this paper, we present a novel visualization technique called route-zooming that can embed spatio-temporal information into a map seamlessly for occlusion-free visualization of both spatial and temporal data. The proposed technique can broaden a selected route in a map by deforming the overall road network. We formulate the problem of route-zooming as a nonlinear least squares optimization problem by defining an energy function that ensures the route is broadened successfully on demand while the distortion caused to the road network is minimized. The spatio-temporal information can then be embedded into the route to reveal both spatial and temporal patterns without occluding the spatial context information. The route-zooming technique is applied in two instantiations including an interactive metro map for city tourism and illustrative maps to highlight information on the broadened roads to prove its applicability. We demonstrate the usability of our spatio-temporal visualization approach with case studies on real traffic flow data. We also study various design choices in our method, including the encoding of the time direction and choices of temporal display, and conduct a comprehensive user study to validate our embedded visualization design.

EvoRiver: Visual Analysis of Topic Coopetition on Social Media.

Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., "topic leaders") affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).