<i>Where Can We Help</i>? A Visual Analytics Approach to Diagnosing and Improving Semantic Segmentation of Movable Objects

He, Wenbin; Zou, Lincan; Shekar, Arvind Kumar; Gou, Liang; Liu, Ren

doi:10.1109/tvcg.2021.3114855

Cited by 37 publications

(27 citation statements)

References 36 publications

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“…Thus, the performance of these models is critical. Recently, researchers have started to use visual analytics to evaluate traffic light detection [75], semantic segmentation [76], action prediction models [77], and even entire systems [78].…”

Section: Autonomous Drivingmentioning

confidence: 99%

“…For example, each cell may encode the traffic volume from a source to a destination [58,69,158] or the interconnection between row and column locations [42,121]. Other classic uses of matrix visualizations include showing classification performance (a confusion matrix) [76] and statistics [31,99].…”

Section: Matrixmentioning

confidence: 99%

“…It is also a very familiar concept. Some researchers will add a video component to their visual analytics approaches, allowing experts to verify the conclusions obtained [60,72,[75][76][77]114].…”

Section: Videomentioning

confidence: 99%

“…Representation learning embeds data into a highdimensional space by vectorization [161]. In this Traffic modelling [53,60,64,80] Data cleaning [13,20] Object tracking [114] Heuristic search [65,69,70,93,98] Simulation-based [16,32,87,113,120,124] Mathematical programming [105,116] Indexing [15,17,22 space, adversarial examples can also be generated for adversarial learning [75,76]. The vectors capture the inherent relationships between data.…”

Section: Representation Learningmentioning

confidence: 99%

See 3 more Smart Citations

A survey of urban visual analytics: Advances and future directions

et al. 2022

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Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

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Section: Autonomous Drivingmentioning

confidence: 99%

Section: Matrixmentioning

confidence: 99%

Section: Videomentioning

confidence: 99%

Section: Representation Learningmentioning

confidence: 99%

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A survey of urban visual analytics: Advances and future directions

et al. 2022

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“…While individual instance-level analysis provides detailed low-level analysis, the scale of datasets urges researchers to develop ways to slice and filter datasets, resulting in subgroup-level analysis [20,23,28]. This allows users to specify data subsets based on attributes and perform more fine-grained analysis than at the class-level.…”

Section: Visualization For Machine Learningmentioning

confidence: 99%

DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps

Bertucci¹,

Hamid²,

Anand³

et al. 2022

Preprint

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Fig. 1. With DendroMap, users can explore large-scale image datasets by overviewing the overall distributions and zooming down into hierarchies of image groups at multiple levels of abstraction. In this example, we visualize images of the CIFAR-100 dataset by hierarchically clustering the image representations obtained from a ResNet50 image classification model. (B) DendroMap View displays these clusters of images organized as a hierarchical structure by adapting Treemaps. By clicking on a cluster, a user can interactively (C) Zoom into that image group, revealing subgroups that replace and fill the available space with animation (see the submitted video).The user clicked on a cluster for organism images, which creates distinct subgroups of fish, insects, worms, fruits, and flowers. With (A) Sidebar View, the user can dynamically adjust the number of clusters to be displayed and inspect the class-level statistics.

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TrafPS: A shapley-based visual analytics approach to interpret traffic

Feng,

Jiang,

Wang

et al. 2024

Comp. Visual Media

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Recent achievements in deep learning (DL) have demonstrated its potential in predicting traffic flows. Such predictions are beneficial for understanding the situation and making traffic control decisions. However, most state-of-the-art DL models are considered “black boxes” with little to no transparency of the underlying mechanisms for end users. Some previous studies attempted to “open the black box” and increase the interpretability of generated predictions. However, handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging. To overcome these challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirements from domain experts, we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

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Where Can We Help? A Visual Analytics Approach to Diagnosing and Improving Semantic Segmentation of Movable Objects

Cited by 37 publications

References 36 publications

A survey of urban visual analytics: Advances and future directions

A survey of urban visual analytics: Advances and future directions

DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps

TrafPS: A shapley-based visual analytics approach to interpret traffic

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