This paper studies a two-step alternative that first condenses the video sequence to an informative "frame" and then exploits off-the-shelf image recognition system on the synthetic frame.
In this paper, we decompose the path into a series of training "states" and specify the hyper-parameters, e. g., learning rate and the length of input clips, in each state.
Video content is multifaceted, consisting of objects, scenes, interactions or actions.
This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus.
Ranked #1 on Video Corpus Moment Retrieval on TVR
It is worth noticing that our TokShift transformer is a pure convolutional-free video transformer pilot with computational efficiency for video understanding.
Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe.
In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e. g., computer games) with computer-generated annotations can be adapted to real images.
Ranked #6 on Domain Adaptation on SYNTHIA-to-Cityscapes
A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.
Due to the lack of proper mechanism in locating instances and deriving feature representation, instance search is generally only effective for retrieving instances of known object categories.
This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019.
Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.
Ranked #4 on Action Recognition on UCF101
Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar.
The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student.
A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity.
The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices.
One of the fundamental problems in image search is to learn the ranking functions, i. e., similarity between the query and image.
In many real-world applications, we are often facing the problem of cross domain learning, i. e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain.