2025年2月17日 · Abstract: In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a training-free paradigm that can make use of adaptive temporal compression in latent space. While existing video generative models apply fixed compression rates via pretrained VAE, we observe that real-world video content exhibits substantial temporal non ...

2025年3月6日 · Follow me for fishing reports, waypoints, and other information that can help make your day on the lake a success! Connect via Facebook, Instagram, or email. Read on for the latest fishing reports. I also offer limited guided day trips and up-to-date waypoints where I’ve recently caught fish. See you on the lake or in the field!

Dual-Level Feature Restoration (DLFR). Unlike the standalone implicit low-level feature reconstruction in TFR-Net, DLFR combines both implicit low-level feature reconstruction and explicit high-level feature attraction to more effectively guide EMT to achieve robust representation learning from incomplete multimodal data.

Dynamic Latent Frame Rate VAE (DLFR-VAE) is a training-free paradigm that utilizes adaptive temporal compression in latent space. While existing video generative models apply fixed compression rates via pretrained VAE, we observe that real-world video content exhibits significant temporal non-uniformity, with high-motion segments containing ...

2022年8月16日 · In this paper, we propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR). Concretely, EMT employs utterance-level representations from each modality as the global multimodal context to interact with local unimodal features and mutually promote each other.

2024年1月4日 · DLFR核心：结合隐式低层次特征重构（implicit low-level feature reconstruction）和显式高层次特征吸引（explict high-level feature attraction）。 融合过程：首先利用特定的 单模态特征编码器 1 从 不同模态 2 输入中获取 语篇级模态内特征 3 （全局模态内特征）和 元素级模态内特征 4 （局部模态内特征 5）；然后使用 EMT 捕捉 全局多模态上下文 6 和 局部单模态特征 5 之间的跨模态交互信息；最后结合 语篇级模态内特征 3 和 语篇级模态间特征 …

In this paper, we propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR). Concretely, EMT employs utterance-level representations from each modality as the global multimodal context to interact with local unimodal features and mutually promote each other.

2025年2月17日 · In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a training-free paradigm that can make use of adaptive temporal compression in latent space. While existing video generative models apply fixed compression rates via pretrained VAE, we observe that real-world video content exhibits substantial temporal non-uniformity, with ...

2023年6月12日 · 其中DLFR: 在incomplete modality setting中增加模型鲁棒性，使用DLFR – low-level feature reconstruction：用来implicitly鼓励模型从incomplete data中学习semantic information – high-level representations：将complete and incomplete data视为一个sample的2个view，使用siamese representation learning来explicitly ...

EMT-DLFR aims to address the inefficiency in fusing unaligned multimodal sequences and the vulnerability to missing data in real-world scenarios to achieve efficient and robust multimodal sentiment analysis.