3 accepted papers
Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in embodied applications, whether they genuinely understand physical transformations remains unclear. We introduce ***ConservationBench*** evaluating ***conse…
While Multi-modal Large Language Models (MLLMs) demonstrate impressive abilities over high-level perception and reasoning, their robustness in the wild remains limited, often falling short on tasks that are intuitive and effortless for humans. We examine the hypothesis that these deficiencies stem f…
Although MOTIP is the SOTA multi-object tracking method, there are still some issues that limit its performance. First, MOTIP still has defects in temporal information modeling, which leads to the failure to fully utilize the historical information of the tracked target and affects the correlation p…