6 citations · 12 across the 6 of their papers we have counts for
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Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation
Inclusion AI, :, Bowen Ma +73
We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which on…
SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrieval
Xuzheng Yu, Chen Jiang, Xingning Dong +3
The user base of short video apps has experienced unprecedented growth in recent years, resulting in a significant demand for video content analysis. In particular, text-video retr…
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…
EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints
Yutao Chen, Xingning Dong, Tian Gan +3
Motivated by the superior performance of image diffusion models, more and more researchers strive to extend these models to the text-based video editing task. Nevertheless, current…
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation
Xingning Dong, Tian Gan, Xuemeng Song +3
Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compa…