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Found 5 papers, 3 papers with code
Lumiere: A Space-Time Diffusion Model for Video Generation
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #6 on
Text-to-Video Generation
on UCF-101
Teaching CLIP to Count to Ten
Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count.
The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
While common and easily reproduced in more general settings, the Slingshot Mechanism does not follow from any known optimization theories that we are aware of, and can be easily overlooked without an in depth examination.
No Token Left Behind: Explainability-Aided Image Classification and Generation
To mitigate it, we present a novel explainability-based approach, which adds a loss term to ensure that CLIP focuses on all relevant semantic parts of the input, in addition to employing the CLIP similarity loss used in previous works.
Image-Based CLIP-Guided Essence Transfer
We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target.
