5 citations · 6 across the 3 of their papers we have counts for
9 papers
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding
Wei Wu, Kecheng Zheng, Shuailei Ma +7
Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key rea…
HOTVCOM: Generating Buzzworthy Comments for Videos
Yuyan Chen, Yiwen Qian, Songzhou Yan +6
In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and br…
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Qingpei Guo, Furong Xu, Hanxiao Zhang +6
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence. Nevertheless, VLM models supporting multi-language, e.g., in both Chinese and…
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
Xingning Dong, Zipeng Feng, Chunluan Zhou +3
We present a Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards effective and efficient zero-shot video-text retrieval, dubbed M2-RAAP. Upon popular image-text…
SNP-S3: Shared Network Pre-training and Significant Semantic Strengthening for Various Video-Text Tasks
Xingning Dong, Qingpei Guo, Tian Gan +5
We present a framework for learning cross-modal video representations by directly pre-training on raw data to facilitate various downstream video-text tasks. Our main contributions…
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
Ziping Ma, Furong Xu, Jian Liu +2
Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, inte…