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Found 8 papers, 5 papers with code
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently.
SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation
It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding.
Deep Image Style Transfer from Freeform Text
The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output.
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.
Graph Condensation via Receptive Field Distribution Matching
Thus, we propose Graph Condesation via Receptive Field Distribution Matching (GCDM), which is accomplished by optimizing the synthetic graph through the use of a distribution matching loss quantified by maximum mean discrepancy (MMD).
Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation
Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning.
VCGAN: Video Colorization with Hybrid Generative Adversarial Network
We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning.
