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Found 9 papers, 4 papers with code
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
Soundini: Sound-Guided Diffusion for Natural Video Editing
We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting.
LISA: Localized Image Stylization with Audio via Implicit Neural Representation
We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization.
Robust Sound-Guided Image Manipulation
Our extensive experiments show that our sound-guided image manipulation approach produces semantically and visually more plausible manipulation results than the state-of-the-art text and sound-guided image manipulation methods, which are further confirmed by our human evaluations.
Sound-Guided Semantic Video Generation
The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation.
Sound-Guided Semantic Image Manipulation
Our audio encoder is trained to produce a latent representation from an audio input, which is forced to be aligned with image and text representations in the multi-modal embedding space.
Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks
However, unsupervised multi-task learning can be biased to a specific task.
Self-supervised Knowledge Distillation Using Singular Value Decomposition
To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed.
