The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a
model trained on this synthetic set will perform equally well as a model trained on the full …

Dataset distillation, which condenses large-scale datasets into compact synthetic
representations, has emerged as a critical solution for training modern deep learning …

Web-scale training on paired text-image data is becoming increasingly central to multimodal
learning, but is challenged by the highly noisy nature of datasets in the wild. Standard data …

Data-efficient learning has garnered significant attention, especially given the current trend
of large multi-modal models. Recently, dataset distillation has become an effective approach …

In text-based person search endeavors, data generation has emerged as a prevailing
practice, addressing concerns over privacy preservation and the arduous task of manual …

In this article, we introduce\textit {audio-visual dataset distillation}, a task to construct a
smaller yet representative synthetic audio-visual dataset that maintains the cross-modal …

Dataset condensation has made significant progress in the image domain. Unlike images,
videos possess an additional temporal dimension, which harbors considerable redundant …

There is a wide connection between linguistics and artificial intelligence (AI), including the
multimodal language matching. Multi-modal robots possess the capability to process various …

Dataset distillation is often used to create compact datasets that can be used to achieve
similar training performance, making it a good choice for addressing the challenges of data …