Towards Visual Explainable Active Learning for Zero-Shot Classification

DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples

Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microenvironments) variations, only the first of which must be removed. To address this issue, we propose Polyphony, an interactive transfer learning (ITL) framework, to complement biologists' knowledge with advanced computational methods. Polyphony is motivated and guided by domain experts' needs for a controllable, interactive, and algorithm-assisted annotation process, identified through interviews with seven biologists. We introduce anchors, i.e., analogous cell populations across datasets, as a paradigm to explain the computational process and collect user feedback for model improvement. We further design a set of visualizations and interactions to empower users to add, delete, or modify anchors, resulting in refined cell type annotations. The effectiveness of this approach is demonstrated through quantitative experiments, two hypothetical use cases, and interviews with two biologists. The results show that our anchor-based ITL method takes advantage of both human and machine intelligence in annotating massive single-cell datasets.

Towards Better Caption Supervision for Object Detection

As training high-performance object detectors requires expensive bounding box annotations, recent methods resort to free-available image captions. However, detectors trained on caption supervision perform poorly because captions are usually noisy and cannot provide precise location information. To tackle this issue, we present a visual analysis method, which tightly integrates caption supervision with object detection to mutually enhance each other. In particular, object labels are first extracted from captions, which are utilized to train the detectors. Then, the objects detected from images are fed into caption supervision for further improvement. To effectively loop users into the object detection process, a node-link-based set visualization supported by a multi-type relational co-clustering algorithm is developed to explain the relationships between the extracted labels and the images with detected objects. The co-clustering algorithm clusters labels and images simultaneously by utilizing both their representations and their relationships. Quantitative evaluations and a case study are conducted to demonstrate the efficiency and effectiveness of the developed method in improving the performance of object detectors.