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Found 12 papers, 5 papers with code
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
We show that the combination of spatially distilled U-Net and fine-tuned decoder outperforms state-of-the-art methods requiring 200 steps with only one single step.
Graph Guided Question Answer Generation for Procedural Question-Answering
The key technological enabler is a novel mechanism for automatic question-answer generation from procedural text which can ingest large amounts of textual instructions and produce exhaustive in-domain QA training data.
GePSAn: Generative Procedure Step Anticipation in Cooking Videos
While most previous work focus on the problem of data scarcity in procedural video datasets, another core challenge of future anticipation is how to account for multiple plausible future realizations in natural settings.
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos
This motivates the need to temporally localize the instruction steps in such videos, i. e. the task called key-step localization.
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization
In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video.
P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision
Our model is based on a transformer equipped with a memory module, which maps the start and goal observations to a sequence of plausible actions.
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
In our experiments, we show that Drop-DTW is a robust similarity measure for sequence retrieval and demonstrate its effectiveness as a training loss on diverse applications.
Representation Learning via Global Temporal Alignment and Cycle-Consistency
We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e. g., videos) of the same process (e. g., human action).
Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support
A standard explanation of this result is that these filters reflect the structure of the images that they have been exposed to during training: Natural images typically are locally composed of oriented contours at various scales and oriented bandpass filters are matched to such structure.
A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding
This paper introduces a new large scale dynamic texture dataset.
What Do We Understand About Convolutional Networks?
This document will review the most prominent proposals using multilayer convolutional architectures.
A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition
Another key aspect of the network is its recurrent nature, whereby the output of each layer of processing feeds back to the input.
