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Found 15 papers, 5 papers with code
Object-Centric Diffusion for Efficient Video Editing
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts.
VaLID: Variable-Length Input Diffusion for Novel View Synthesis
This is because the source-view images and corresponding poses are processed separately and injected into the model at different stages.
Clockwork Diffusion: Efficient Generation With Model-Step Distillation
This work aims to improve the efficiency of text-to-image diffusion models.
ResQ: Residual Quantization for Video Perception
Furthermore, we extend our model to dynamically adjust the bit-width proportional to the amount of changes in the video.
Skip-Attention: Improving Vision Transformers by Paying Less Attention
This work aims to improve the efficiency of vision transformers (ViT).
Simple and Efficient Architectures for Semantic Segmentation
Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware.
SALISA: Saliency-based Input Sampling for Efficient Video Object Detection
In this paper, we propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection that allows for heavy down-sampling of unimportant background regions while preserving the fine-grained details of a high-resolution image.
Delta Distillation for Efficient Video Processing
By extensive experiments on a wide range of architectures, including the most efficient ones, we demonstrate that delta distillation sets a new state of the art in terms of accuracy vs. efficiency trade-off for semantic segmentation and object detection in videos.
Ranked #2 on
Video Semantic Segmentation
on Cityscapes val
Region-of-Interest Based Neural Video Compression
To the best of our knowledge, our proposals are the first solutions that integrate ROI-based capabilities into neural video compression models.
FrameExit: Conditional Early Exiting for Efficient Video Recognition
In this paper, we propose a conditional early exiting framework for efficient video recognition.
Skip-Convolutions for Efficient Video Processing
We reformulate standard convolution to be efficiently computed on residual frames: each layer is coupled with a binary gate deciding whether a residual is important to the model prediction,~\eg foreground regions, or it can be safely skipped, e. g. background regions.
Video Compression With Rate-Distortion Autoencoders
We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressive prior used for entropy coding.
Learning Variations in Human Motion via Mix-and-Match Perturbation
In this paper, we introduce an approach to stochastically combine the root of variations with previous pose information, which forces the model to take the noise into account.
VideoStory Embeddings Recognize Events when Examples are Scarce
In our proposed embedding, which we call VideoStory, the correlations between the terms are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the VideoStory using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation.
