4 accepted papers
Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, howe…
The many variations of Implicit Neural Representations (INRs) where a neural network is trained as a continuous representation of a signal have tremendous practical utility for downstream tasks including novel view synthesis video compression and image super-resolution. Unfortunately the inner worki…
Implicit neural representations store videos as neural networks and have performed well for vision tasks such as video compression and denoising. With frame index and/or positional index as input, implicit representations (NeRV, E-NeRV, etc.) reconstruct video frames from fixed and content-agnostic…
By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field -- many methods with substantially different implementations yield results that…