Recent advancement in multimodal LLMs (MLLMs) has demonstrated their remarkable capability to generate descriptive captions for input videos. However, these models suffer from factual inaccuracies in the generated descriptions, causing severe hallucination issues. While prior works have explored alleviating hallucinations for static images, jointly mitigating visual object and temporal action hallucinations for dynamic videos remains a challenging and unsolved task. To tackle this challenge, we propose a Self-Augmented Contrastive Alignment (SANTA) framework for enabling object and action faithfulness by exempting the spurious correlations and enforcing the emphasis on visual facts. SANTA employs a hallucinative self-augmentation scheme to identify the potential hallucinations that lie in the MLLM and transform the original captions to the contrasted negatives. Furthermore, we develop a tracklet-phrase contrastive alignment to match the regional objects and relation-guided actions with their corresponding visual and temporal phrases. Extensive experiments demonstrate that SANTA outperforms existing methods in alleviating object and action hallucinations, yielding superior performance on the hallucination examination benchmarks.

翻译：多模态大语言模型（MLLMs）的最新进展展示了其为输入视频生成描述性字幕的卓越能力。然而，这些模型在生成描述时存在事实性错误，导致严重的幻觉问题。尽管先前的研究已探索缓解静态图像中的幻觉，但针对动态视频同时减轻视觉物体幻觉与时序动作幻觉仍是一项具有挑战性且尚未解决的任务。为应对这一挑战，我们提出了一种自增强对比对齐（SANTA）框架，通过排除虚假关联并强化对视觉事实的关注，以实现物体与动作的忠实性。SANTA采用一种幻觉性自增强方案，以识别MLLM中潜在的幻觉，并将原始字幕转换为对比负样本。此外，我们开发了一种轨迹-短语对比对齐方法，将区域物体和关系引导的动作与其对应的视觉及时序短语进行匹配。大量实验表明，SANTA在缓解物体与动作幻觉方面优于现有方法，在幻觉检测基准上取得了更优的性能。