Deep neural networks (DNNs) have proven explicit contributions in making autonomous
driving cars and related tasks such as semantic segmentation, motion tracking, object …

In recent years, social media has significantly influenced how we share information and
exchange messages. However, a significant issue arises from the fast dissemination of …

Abstract Large Language Models have demonstrated remarkable performance across
various tasks exhibiting the capacity to swiftly acquire new skills such as through In-Context …

Abstract Domain generalization aims to solve the challenge of Out-of-Distribution (OOD)
generalization by leveraging common knowledge learned from multiple training domains to …

For multimodal LLMs, the synergy of visual comprehension (textual output) and generation
(visual output) presents an ongoing challenge. This is due to a conflicting objective: for …

Large language models are well-known to be effective at few-shot in-context learning (ICL).
Recent advancements in multimodal foundation models have enabled unprecedentedly …

Temporal Sentence Grounding (TSG), which aims to localize events in untrimmed videos
with a given language query, has been widely studied in the last decades. However …

Top-view perspective denotes a typical way in which humans read and reason over different
types of maps, and it is vital for localization and navigation of humans as well as ofnon …

Large language models exhibit a remarkable capacity for in-context learning, where they
learn to solve tasks given a few examples. Recent work has shown that transformers can be …

Large language models (LLMs), with demonstrated reasoning abilities across multiple
domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in …