Federico Rossano

dblp:229/8653 · DBLP profile ↗
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9ranked-venue papers
0as first author
8since 2021 · last 2026
0000-0002-6544-7685ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 8 · 8 since 2021Applied, interdisciplinary, general and emerging computing · 5 · 5 since 2021Human-computer interaction and ubiquitous computing · 1
YearPublicationVenuePosition
2026 AlphaChimp: Tracking and Behavior Recognition of Chimpanzees
Xiaoxuan Ma 0001, Yutang Lin 0001, Yuan Xu 0022, Stephan P. Kaufhold, Jack Terwilliger, Andres Meza 0001, Yixin Zhu 0001, Federico Rossano, Yizhou Wang 0001
Int. J. Comput. Vis.8
2025 Socially Situated Navigation: Social Rank and Sex Influence Spatial Navigation Strategies in Japanese Macaques
Stephan P. Kaufhold, Jack Terwilliger, Federico Rossano
CogSci3
2025 Initiation Asymmetry in the Ontogenesis of Social Routines: In Conversation, Caregivers Scaffold 1-Year Olds to Respond, but 2-year Olds Initiate
Jack Terwilliger, Federico Rossano
CogSci2
2023 Understanding Embodied Reference with Touch-Line Transformer
Yang Li 0178, Xiaoxue Chen, Hao Zhao 0002, Jiangtao Gong, Guyue Zhou, Federico Rossano, Yixin Zhu 0001
ICLR6
2023 ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
abstract
Understanding the behavior of non-human primates is crucial for improving animal welfare, modeling social behavior, and gaining insights into distinctively human and phylogenetically shared behaviors. However, the lack of datasets on non-human primate behavior hinders in-depth exploration of primate social interactions, posing challenges to research on our closest living relatives. To address these limitations, we present ChimpACT, a comprehensive dataset for quantifying the longitudinal behavior and social relations of chimpanzees within a social group. Spanning from 2015 to 2018, ChimpACT features videos of a group of over 20 chimpanzees residing at the Leipzig Zoo, Germany, with a particular focus on documenting the developmental trajectory of one young male, Azibo. ChimpACT is both comprehensive and challenging, consisting of 163 videos with a cumulative 160,500 frames, each richly annotated with detection, identification, pose estimation, and fine-grained spatiotemporal behavior labels. We benchmark representative methods of three tracks on ChimpACT: (i) tracking and identification, (ii) pose estimation, and (iii) spatiotemporal action detection of the chimpanzees. Our experiments reveal that ChimpACT offers ample opportunities for both devising new methods and adapting existing ones to solve fundamental computer vision tasks applied to chimpanzee groups, such as detection, pose estimation, and behavior analysis, ultimately deepening our comprehension of communication and sociality in non-human primates.
Xiaoxuan Ma 0001, Stephan P. Kaufhold, Jiajun Su, Wentao Zhu 0004, Jack Terwilliger, Andres Meza 0001, Yixin Zhu 0001, Federico Rossano, Yizhou Wang 0001
NeurIPS8
2022 What Is the point? a Theory of Mind Model of Relevance
Stephanie Stacy, Annya L. Dahmani, Boxuan Jiang, Federico Rossano, Yixin Zhu 0001, Tao Gao 0004
CogSci5
2021 Individual vs. Joint Perception: a Pragmatic Model of Pointing as Smithian Helping
Stephanie Stacy, Adelpha Chan, Chuyu Wei, Federico Rossano, Yixin Zhu 0001, Tao Gao 0004
CogSci5
2021 Sharing is not Needed: Modeling Animal Coordinated Hunting with Reinforcement Learning
Minglu Zhao, Annya L. Dahmani, Ross Richard Perry, Yixin Zhu 0001, Federico Rossano, Tao Gao 0004
CogSci6
2018 Score-Group Framing Negatively Impacts Peer Evaluations
abstract
How does group membership framing affect the feedback students provide learners? This paper presents two between-subjects experiments investigating the effect of Ingroup/Outgroup membership on effort spent in peer evaluations, and whether the group membership criterion type affects quality and stringency of evaluation. Two peer-review assignments were implemented in two separate classes. In the first study, students were nominally grouped by location they sat in class and non-nominally grouped by current class score; each was asked to review an Ingroup and Outgroup peer assignment. A second study randomly assigned students to one of four group types (random, score, motivation, and location); student reviewed two Ingroup assignments. In both studies, score-grouped students graded their peers more stringently than students grouped by location. These studies illustrate for system designers the impacts of group framing - and the disclosure of that-in peer review tasks.
Celia Durkin, Federico Rossano, Scott R. Klemmer
Proc. ACM Hum. Comput. Interact.2