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Irene Testini

dblp:374/6500 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-4983-1844ORCID · reported

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
Segmentation and scene understanding · 77% 3D vision · 23%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding › scene understanding
panoptic scene understanding
0.812024
360+x: A Panoptic Multi-modal Scene Understanding Dataset · CVPR 2024
Computer vision › 3D vision › omnidirectional vision
panoramic vision
0.212024
360+x: A Panoptic Multi-modal Scene Understanding Dataset · CVPR 2024

Methods — techniques the papers use, named apart from their topics

benchmark analysis · 1.5
YearPublicationVenuePosition
2024 360+x: A Panoptic Multi-modal Scene Understanding Dataset
abstract
Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views), our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically, we encapsulate third-person panoramic and front views, as well as egocentric monocular/binocular views with rich modalities including video, multi-channel audio, directional binaural delay, location data and textual scene descriptions within each scene captured, presenting comprehensive observation of the world. To the best of our knowledge, this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis, we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.
Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo Jiao
CVPR4