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MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
Bo He;Hengduo Li;Young Kyun Jang;Menglin Jia;Xuefei Cao;Ashish Shah;Abhinav Shrivastava;Ser-Nam Lim
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding. In this study, we mainly focus on designing an efficient and effective...Show More
Content-Aware Image Color Editing with Auxiliary Color Restoration Tasks
Yixuan Ren;Jing Shi;Zhifei Zhang;Yifei Fan;Zhe Lin;Bo He;Abhinav Shrivastava
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2024 | Conference Paper |
Diversified image color editing is typically modeled as a multimodal image-to-image translation (MMI2IT) problem with an impact on multiple applications such as photo enhancement and retouching. Although previous GAN-based algorithms successfully generate diverse edits with clear control, we observe two issues remaining: Firstly, they tend to apply the same color style to all kinds of input images...Show More
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
Bo He;Jun Wang;Jielin Qiu;Trung Bui;Abhinav Shrivastava;Zhaowen Wang
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (26)
The goal of multimodal summarization is to extract the most important information from different modalities to form summaries. Unlike unimodal summarization, the multimodal summarization task explicitly leverages cross-modal information to help generate more reliable and high-quality summaries. However, existing methods fail to lever-age the temporal correspondence between different modal-ities an...Show More
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Bo He;Xitong Yang;Le Kang;Zhiyu Cheng;Xin Zhou;Abhinav Shrivastava
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (68)
Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i.e., video snippets) are supervised by classifying labeled bags (i.e....Show More
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