Large Video Models (LVMs) built upon Large Language Models (LLMs) have shown
promise in video understanding but often suffer from misalignment with human
intuition and video hallucination issues. To address these challenges, we
introduce VistaDPO, a novel framework for Video Hierarchical Spatial-Temporal
Direct Preference Optimization. VistaDPO enhances text-video preference
alignment across three hierarchical levels: i) Instance Level, aligning overall
video content with responses; ii) Temporal Level, aligning video temporal
semantics with event descriptions; and iii) Perceptive Level, aligning spatial
objects with language tokens. Given the lack of datasets for fine-grained
video-language preference alignment, we construct VistaDPO-7k, a dataset of
7.2K QA pairs annotated with chosen and rejected responses, along with
spatial-temporal grounding information such as timestamps, keyframes, and
bounding boxes. Extensive experiments on benchmarks such as Video
Hallucination, Video QA, and Captioning performance tasks demonstrate that
VistaDPO significantly improves the performance of existing LVMs, effectively
mitigating video-language misalignment and hallucination. The code and data are
available at https://github.com/HaroldChen19/VistaDPO.