Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tirupati Pallewad
dblp:324/1269
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Video understanding and tracking · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Video understanding and tracking › action recognition
action recognition and localization |
0.6 | 1 | 2022 | C3D and Localization Model for Locating and Recognizing the Actions from Untrimmed Videos (Student Abstract) · AAAI 2022 |
Computer vision › Video understanding and tracking
untrimmed video understanding |
0.2 | 1 | 2022 | C3D and Localization Model for Locating and Recognizing the Actions from Untrimmed Videos (Student Abstract) · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
pseudo-labeling · 0.6k-NN classifier · 0.6C3D CNN · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | C3D and Localization Model for Locating and Recognizing the Actions from Untrimmed Videos (Student Abstract)abstractIn this article, we proposed a technique for action localization and recognition from long untrimmed videos. It consists of C3D CNN model followed by the action mining using the localization model, where the KNN classifier is used. We segment the video into expressible sub-action known as action-bytes. The pseudo labels have been used to train the localization model, which makes the trimmed videos untrimmed for action-bytes. We present experimental results on the recent benchmark trimmed video dataset “Thumos14”. Himanshu Singh 0006, Tirupati Pallewad, Badri N. Subudhi, Vinit Jakhetiya |
AAAI | 2 |