11 accepted papers
Video captioning automatically generates natural language phrases to explain the contents in video frames. When deploying captioning models in specialized domains, active learning can help reduce the high annotation cost. However, the generative nature of the captioning process is more complex than…
Reinforcement learning (RL) provides an effective means to capture users' evolving preferences, leading to improved recommendation performance over time. However, existing RL approaches primarily rely on standard exploration strategies, which are less effective for a large item space with sparse rew…
Sequential recommender systems are designed to capture users' evolving interests over time. Existing methods typically assume a uniform time interval among consecutive user interactions and may not capture users' continuously evolving behavior in the short and long term. In reality, the actual time…
Machine learning-driven human behavior analysis is gaining attention in behavioral/mental healthcare, due to its potential to identify behavioral patterns that cannot be recognized by traditional assessments. Real-life applications, such as digital behavioral biomarker identification, often require…
Medical image captioning alleviates the burden of physicians and possibly reduces medical errors by automatically generating text descriptions to describe image contents and convey findings. It is more challenging than conventional image captioning due to the complexity of medical images and the dif…
Image captioning offers a computational process to understand the semantics of images and convey them using descriptive language. However, automated captioning models may not always generate satisfactory captions due to the complex nature of the images and the quality/size of the training data. We p…
We propose a multimodal data fusion framework to systematically analyze human behavioral data from specialized domains that are inherently dynamic, sparse, and heterogeneous. We develop a two-tier architecture of probabilistic mixtures, where the lower tier leverages parametric distributions from th…
We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critical to maintain the user base of a recommender system. Due to the sparse re…
Image segmentation can be performed interactively by accepting user annotations to refine the segmentation. It seeks frequent feedback from humans, and the model is updated with a smaller batch of data in each iteration of the feedback loop. Such a training paradigm requires effective information fi…
Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but the results provided by fully automatic algorithms are not always guaranteed satisfactory to users. Interactive segmentation offers a solution by accepting user annotations on selective areas of the i…
We propose to jointly analyze experts' eye movements and verbal narrations to discover important and interpretable knowledge patterns to better understand their decision-making processes. The discovered patterns can further enhance data-driven statistical models by fusing experts' domain knowledge t…