Visual perspective-taking, an ability to envision others’ perspectives from a single self-perspective, is vital in human-robot interactions. Thus, we introduce a human-centric visual grounding task and a dataset to evaluate this ability. Recent advances in vision-language models (VLMs) have shown potential for inferring others’ perspectives, yet are insensitive to information differences induced by slight perspective changes. To address this problem, we propose a top-view enhanced perspective transformation (TEP) method, which decomposes the transition from robot to human perspectives through an abstract top-view representation. It unifies perspectives and facilitates the capture of information differences from diverse perspectives. Experimental results show that TEP improves performance by up to 18%, exhibits perspective-taking abilities across various perspectives, and generalizes effectively to robotic and dynamic scenarios.

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT’s performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4.

Knowledge-based referring expression comprehension (KB-REC) aims to identify visual objects referred to by expressions that incorporate knowledge. Existing methods employ sentence-level retrieval and fusion methods, which may lead to issues of similarity bias and interference from irrelevant information in unstructured knowledge sentences. To address these limitations, we propose a segment-level and category-oriented network (SLCO). Our approach includes a segment-level and prompt-based knowledge retrieval method to mitigate the similarity bias problem and a category-based grounding method to alleviate interference from irrelevant information in knowledge sentences. Experimental results show that our SLCO can eliminate interference and improve the overall performance of the KB-REC task.