Add to ORKGThis document contains supplementary material for the main paper [1]. We first describe the inference and learn-ing procedures for the temporal SCRF and STRF models in more detail in Sections 1, 2 and then show additional quali-tative results comparing STRF to baselines in Section 3
The analysis of visual motion against dense background clutter is a challenging problem. Uncertainty...
The semantic interpretation of video sequences by computer is often formulated as probabilistically ...
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...
We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to ...
We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to ...
How should a video be represented? We propose a new representation for videos based on mid-level dis...
In this dissertation, I present my work towards exploring temporal information for better video unde...
According to some current thinking, a very large number of semantic concepts could provide researche...
We study novel learning and inference algorithms for temporal, relational data and their application...
In this supplementary material, we provide details for the following: (1) multi-scale video segmenta...
There has been a tremendous growth in publicly available digital video footage over the past decade....
This work presents a first evaluation of using spatio-temporal receptive fields from a recently prop...
The task of video grounding, which temporally localizes a natural language description in a video, p...
We first propose a new spatio-temporal context distri-bution feature of interest points for human ac...
In this paper we present the application of hidden conditional random fields (HCRFs) to modeling spe...
The analysis of visual motion against dense background clutter is a challenging problem. Uncertainty...
The semantic interpretation of video sequences by computer is often formulated as probabilistically ...
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...
We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to ...
We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to ...
How should a video be represented? We propose a new representation for videos based on mid-level dis...
In this dissertation, I present my work towards exploring temporal information for better video unde...
According to some current thinking, a very large number of semantic concepts could provide researche...
We study novel learning and inference algorithms for temporal, relational data and their application...
In this supplementary material, we provide details for the following: (1) multi-scale video segmenta...
There has been a tremendous growth in publicly available digital video footage over the past decade....
This work presents a first evaluation of using spatio-temporal receptive fields from a recently prop...
The task of video grounding, which temporally localizes a natural language description in a video, p...
We first propose a new spatio-temporal context distri-bution feature of interest points for human ac...
In this paper we present the application of hidden conditional random fields (HCRFs) to modeling spe...
The analysis of visual motion against dense background clutter is a challenging problem. Uncertainty...
The semantic interpretation of video sequences by computer is often formulated as probabilistically ...
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...