本文在总结贡献点时,提出了三个:1统一模型可以覆盖多个多模态任务的框架;2.提出了一种多模态共享离散语言空间的方法,这里把学到的表征叫做 modality-agnostic linguistic representation;3. 本模型经过表征学习和解耦之后可以进行音视频信号的转换和操纵。 本文提出的具体方法参考下面这张图: 首先,模型针对音频和视频模态...
Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-...
To solve this problem, we propose a cross-modality consistency learning network, which jointly considers crossmodal learning and distillation learning. It consists of two associated components: the feature adaptation network (FANet) and the modality learning module (MLM). The FANet combines global and...
While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose cross-modal learning, where we enforce consistency between the predictions of two modalities via mutual mimicking. We constrain our...
Our framework is tailored to the unimodal segmentation of the T1ce MRI sequence, which is prevalently available in clinical practice and structurally akin to the T1 modality, providing ample information for the segmentation task. Our framework introduces two learning strategies for knowledge distillation...
3.2. Multimodal Robust Learning The goal of cross-modal retrieval is to retrieve the corre- lated samples across different modalities in a common rep- resentation space Z. To project distinct modalities into Z, existing methods attempt to learn m modality-specific func- tions {fi : Xi → Z...
In principle, M$^3$R is capable of simultaneously accomplishing the following two learning tasks: 1) modality-specific (e.g., image-specific or text-specific ) latent topic learning; and 2) cross-modal mutual topic consistency learning. By investigating the cross-modal topic-related distribution...
(AM3)Adaptive Cross-Modal Few-shot Learning 论文笔记 ,并且根据不同的场景自适应地对它们进行混合,以达到最优效果。由此,本文提出AM3(Adaptive Modality Mixture Mechanism),它能够自适应并且有选择地结合两种模式(视觉和语义...前言 本文提出了一种利用跨模式(cross-modal)信息(视觉特征和语义特征)来增强基于度量...
To this end, we propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs. On the one hand, the neighboring similarity distribution is introduced to model the knowledge learned in each modality, where the relational information is naturally suitable for the contrastive...
Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model (Fig. 2), which tackles the task by a mutual modulation strategy, including...