7 papers
Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning
Qing Lin, Mengmi Zhang
Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states. Despi…
FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification
Qingran Lin, Fengwei Yang, Chaolun Zhu
Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and rea…
Learning to Perceive "Where": Spatial Pretext Tasks for Robust Self-Supervised Learning
Yang Shen, Yusen Cai, Weronika Hryniewska-Guzik +2
Existing self-supervised learning (SSL) methods primarily learn object-invariant representations but often neglect the spatial structure and relationships among object parts. To ad…
Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines
Yusen Cai, Qing Lin, Bhargava Satya Nunna +1
Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of…
MOSIV: Multi-Object System Identification from Videos
Chunjiang Liu, Xiaoyuan Wang, Qingran Lin +9
We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discret…
Unforgettable Lessons from Forgettable Images: Intra-Class Memorability Matters in Computer Vision
Jie Jing, Yongjian Huang, Serena J. -W. Wang +5
We introduce intra-class memorability, where certain images within the same class are more memorable than others despite shared category characteristics. To investigate what featur…