Search Results for author:
Found 12 papers, 6 papers with code
Clockwork Diffusion: Efficient Generation With Model-Step Distillation
This work aims to improve the efficiency of text-to-image diffusion models.
Skip-Attention: Improving Vision Transformers by Paying Less Attention
This work aims to improve the efficiency of vision transformers (ViT).
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.
FrameExit: Conditional Early Exiting for Efficient Video Recognition
In this paper, we propose a conditional early exiting framework for efficient video recognition.
Video Time: Properties, Encoders and Evaluation
Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding.
Actor and Action Video Segmentation from a Sentence
This paper strives for pixel-level segmentation of actors and their actions in video content.
Ranked #13 on
Referring Expression Segmentation
on J-HMDB
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in images and videos is proposed.
Online Action Detection
Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated.
Rank Pooling for Action Recognition
We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation.
Towards Automatic Image Editing: Learning to See another You
Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved.
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps.
Modeling Video Evolution for Action Recognition
We postulate that a function capable of ordering the frames of a video temporally (based on the appearance) captures well the evolution of the appearance within the video.
