AI Breakdown

CVPR 2023 - Align and Attend: Multimodal Summarization with Dual Contrastive Losses

In this episode we discuss Align and Attend: Multimodal Summarization with Dual Contrastive Losses
by Authors:
- Bo He
- Jun Wang
- Jielin Qiu
- Trung Bui
- Abhinav Shrivastava
- Zhaowen Wang
Affiliations:
- Bo He, Jun Wang, and Abhinav Shrivastava: University of Maryland, College Park
- Jielin Qiu: Carnegie Mellon University
- Trung Bui and Zhaowen Wang: Adobe Research. The paper proposes a new approach called Align and Attend Multimodal Summarization (A2Summ) for extracting important information from multiple modalities to create reliable summaries. It introduces a unified transformer-based model that aligns and attends to the multimodal input, while also addressing the issue of ignoring temporal correspondence between different modalities and intrinsic correlation between different samples. The proposed model achieves state-of-the-art performance on standard video summarization and multimodal summarization datasets and the authors also introduce a new large-scale multimodal summarization dataset called BLiSS.

CVPR 2023 - MobileNeRF: Exploiting the Polygon Rasterization Pipeline

Paper titled MobileNeRF: Exploiting the Polygon Rasterization Pipeline. 

The paper was published in CVPR 2023 conference by Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. The paper introduces a new representation of Neural Radiance Fields, called MobileNeRF, that can render 3D scenes at interactive frame rates on a wide range of compute platforms, including mobile phones.

CVPR 2023 - EXIF as Language: Learning Cross-Modal

This episode is about the paper titled “EXIF as Language: Learning Cross-Modal” at CVPR 2023, by Chenhao Zheng, Ayush Shrivastava, and Andrew Owens from University of Michigan. In this paper, the authors propose a new approach to learning visual representations, which captures information about the camera that recorded a given photo.