Skip to yearly menu bar Skip to main content


Poster

PiTe: Pixel-Temporal Alignment for Large Video-Language Model

Yang Liu · Pengxiang Ding · Siteng Huang · Min Zhang · Han Zhao · Donglin Wang

# 181
Strong blind review: This paper was not made available on public preprint services during the review process Strong Double Blind
[ ] [ Paper PDF ]
Thu 3 Oct 1:30 a.m. PDT — 3:30 a.m. PDT

Abstract:

Fueled by the Large Language Models (LLMs) wave, Large Visual-Language Models (LVLMs) have emerged as a pivotal advancement, bridging the gap between image and text. However, video making it challenging for LVLMs to perform adequately due to the complexity of the relationship between language and spatial-temporal data structure. Recent Large Video-Language Models (LVidLMs) align feature of static visual data like image into latent space of language feature, by general multi-modal tasks to leverage abilities of LLMs sufficiently. In this paper, we explore fine-grained alignment approach via object trajectory for different modalities across both spatial and temporal dimensions simultaneously. Thus, we propose a novel LVidLM by trajectory-guided Pixel-Temporal Alignment, dubbed PiTe, that exhibits promising applicable model property. To achieve fine-grained video-language alignment, we curate a multi-modal pre-training dataset PiTe-143k, the dataset provision of moving trajectories in pixel level for all individual objects, that appear and mention in the video and caption both, by our automatic annotation pipeline. Meanwhile, PiTe demonstrates astounding capabilities on myriad video-related multi-modal tasks through beat the state-of-the-art methods by a large margin.

Live content is unavailable. Log in and register to view live content