6 papers · 1 filter
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…
Make Me Happier: Evoking Emotions Through Image Diffusion Models
Qing Lin, Jingfeng Zhang, Yew-Soon Ong +1
Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, mak…