Video-based Large Language Models (Video-LLMs) have witnessed substantial advancements in recent years, propelled by the advancement in multi-modal LLMs. Although these models have demonstrated proficiency in providing the overall description of videos, they struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries. To tackle these shortcomings, we find that fine-tuning Video-LLMs on self-supervised fragment tasks, greatly improve their fine-grained video understanding abilities. Hence we propose two key contributions: (1) Self-Supervised Fragment Fine-Tuning (SF2T), a novel effortless fine-tuning method, employs the rich inherent characteristics of videos for training, while unlocking more fine-grained understanding ability of Video-LLMs. Moreover, it relieves researchers from labor-intensive annotations and smartly circumvents the limitations of natural language, which often fails to capture the complex spatiotemporal variations in videos; (2) A novel benchmark dataset, namely FineVidBench, for rigorously assessing Video-LLMs’ performance at both the scene and fragment levels, offering a comprehensive evaluation of their capabilities. We assessed multiple models and validated the effectiveness of SF2T on them. Experimental results reveal that our approach improves their ability to capture and interpret spatiotemporal details.

Video large language models (video-LLMs) have demonstrated impressive capabilities in multimodal understanding, but their potential as zero-shot evaluators for temporal consistency in video captions remains underexplored. Existing methods notably underperform in detecting critical temporal errors, such as missing, hallucinated, or misordered actions. To address this gap, we introduce two key contributions. (1) TimeJudge: a novel zero-shot framework that recasts temporal error detection as answering calibrated binary question pairs. It incorporates modality-sensitive confidence calibration and uses consistency-weighted voting for robust prediction aggregation. (2) TEDBench: a rigorously constructed benchmark featuring videos across four distinct complexity levels, specifically designed with fine-grained temporal error annotations to evaluate video-LLM performance on this task. Through a comprehensive evaluation of multiple state-of-the-art video-LLMs on TEDBench, we demonstrate that TimeJudge consistently yields substantial gains in terms of recall and F1-score without requiring any task-specific fine-tuning. Our approach provides a generalizable, scalable, and training-free solution for enhancing the temporal error detection capabilities of video-LLMs.

A method that allows quick estimation of Redirection Detection Thresholds (RDTs) is not only useful for identifying factors that contribute to the detection of redirections, but can also provide timely inputs for personalized redirected walking control. In aim to achieve quick RDT estimation, we opted for a classical psychophysical method - the Method of Adjustment (MoA), and compared it against commonly used method for RDT estimation (i.e. MCS-2AFC) to see their difference. Preliminary results show that MoA allows to save about 33% experiment time when compared with MCS-2AFC while getting overall similar RDT estimations on the same population.