Search Results for author:
Found 5 papers, 3 papers with code
Animated Stickers: Bringing Stickers to Life with Video Diffusion
Our model is built on top of the state-of-the-art Emu text-to-image model, with the addition of temporal layers to model motion.
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression
Evaluation results show our method improves visual quality by 14%, prompt alignment by 16. 2% and scene diversity by 15. 3%, compared to prompt engineering the base Emu model for stickers generation.
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP).
Connecting What to Say With Where to Look by Modeling Human Attention Traces
Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded.
What's the Point: Semantic Segmentation with Point Supervision
The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost.
