Towards Better Caption Supervision for Object Detection

Chen, Changjian; Wu, Jing; Wang, Xiaohan; Xiang, Shouxing; Zhang, Song-Hai; Tang, Qifeng; Liu, Shixia

doi:10.1109/tvcg.2021.3138933

Cited by 13 publications

(1 citation statement)

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“…To enable efficient examination of the relevant samples, a grid visualization is employed because of its effectiveness in exploring image content [14,15,51]. The cells of the grid display selected samples with their predictions.…”

Section: Grid Visualizationmentioning

confidence: 99%

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

Chen,

Guo,

Tian

et al. 2023

IEEE Trans. Visual. Comput. Graphics

Self Cite

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Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.

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Section: Grid Visualizationmentioning

confidence: 99%

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

Chen,

Guo,

Tian

et al. 2023

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

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A Personalized Captioning Strategy for the Deaf and Hard-of-Hearing Users in an Augmented Reality Environment

Shidende,

Kessel,

Treydte

et al. 2024

Lecture Notes in Computer Science

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Foundation models meet visualizations: Challenges and opportunities

Yang,

Liu,

Wang

et al. 2024

Comp. Visual Media

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Recent studies have indicated that foundation models, such as BERT and GPT, excel at adapting to various downstream tasks. This adaptability has made them a dominant force in building artificial intelligence (AI) systems. Moreover, a new research paradigm has emerged as visualization techniques are incorporated into these models. This study divides these intersections into two research areas: visualization for foundation model (VIS4FM) and foundation model for visualization (FM4VIS). In terms of VIS4FM, we explore the primary role of visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FM addresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in terms of FM4VIS, we highlight how foundation models can be used to advance the visualization field itself. The intersection of foundation models with visualizations is promising but also introduces a set of challenges. By highlighting these challenges and promising opportunities, this study aims to provide a starting point for the continued exploration of this research avenue.

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Towards Better Caption Supervision for Object Detection

Cited by 13 publications

References 50 publications

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

A Personalized Captioning Strategy for the Deaf and Hard-of-Hearing Users in an Augmented Reality Environment

Foundation models meet visualizations: Challenges and opportunities

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