PiTe: Pixel-Temporal Alignment for Large Video-Language Model
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.
66e40348038300b07a7dda0f
Yang Liu
claimed_verified
2025-05-21T09:10:53.960Z
6586817e509bcae23f3dfc60
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pwEStoey1XJYDi3F0Z930.png
Yang Liu
user
yliu-cs
66e40348038300b07a7dda10
Pengxiang Ding
66e40348038300b07a7dda11
Siteng Huang
claimed_verified
2024-09-13T12:09:08.813Z
65fd82762bf2cd20ddaa193f
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yBYbWp_mT7UusYdkqtAvw.png
Siteng Huang
user
huangsiteng
66e40348038300b07a7dda12
Min Zhang
66e40348038300b07a7dda13
Han Zhao
66e40348038300b07a7dda14
Donglin Wang
PiTe: Pixel-Temporal Alignment for Large Video-Language Model
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.
A new video-language model, PiTe, uses trajectory-guided pixel-temporal alignment to achieve superior performance across various multimodal video tasks by leveraging a large pre-training dataset with precise object trajectories.
Large Language Models (LLMs) Large Visual-Language Models (LVLMs) video spatial-temporal data Large Video-Language Models (LVidLMs) latent space multi-modal tasks fine-grained alignment object trajectory trajectory-guided Pixel-Temporal Alignment PiTe pre-training dataset PiTe-143k automatic annotation pipeline