Search Results for author:
Found 7 papers, 5 papers with code
UniVG: Towards UNIfied-modal Video Generation
This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination.
Accommodating Audio Modality in CLIP for Multimodal Processing
In this paper, we extend the stateof-the-art Vision-Language model CLIP to accommodate the audio modality for Vision-Language-Audio multimodal processing.
TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World
Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall.
Survey: Transformer based Video-Language Pre-training
Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have begun to apply transformer to video processing.
Team RUC_AIM3 Technical Report at ActivityNet 2021: Entities Object Localization
For the object grounding, we fine-tune the state-of-the-art detection model MDETR and design a post processing method to make the grounding results more faithful.
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e. g. makeup instructional videos.
