10 accepted papers
We introduce a new context-enriched, multimodal time series forecasting benchmark TimesX. TimesX contains a wide selection of high-quality real-world time series with diverse domains and textual contexts obtained from an automated data generation pipeline, which helps address three main issues of ex…
As delivery robots are increasingly integrated into our daily lives, their ability to navigate through crowded spaces demands swift and accurate prediction of pedestrian trajectories, which is crucial for autonomous functionality. However, existing methods face challenges of unstable accuracy and in
Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for _in-context fine-tuning_ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-seri…
Accurate Six Degrees of Freedom (6-DoF) pose estimation of Ship-To-Shore (STS) quay crane spreaders is crucial for ensuring safe and efficient container handling in port automation. However, existing pose estimation techniques face significant challenges, as camera-based systems either rely on marke
Accurate Relative Pose Estimation (RPE) is critical for effective collaboration of multi-robot systems. Traditional methods using cameras or LiDARs heavily rely on overlapping Fields of View (FoV) between robots, which is highly demanding in practical applications and may hinder collaboration effici
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting mode…
Knowledge distillation is a popular technique that has been shown to produce remarkable gains in average accuracy. However, recent work has shown that these gains are not uniform across subgroups in the data, and can often come at the cost of accuracy on rare subgroups and classes. Robust optimizati…
Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFo…
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perf…
In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints. In particular, with ranking fairness tasks, there may even be a variable number of constraints, e.g. one for eac…