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Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto close-by internal representations. When considering this…

Machine Learning · Computer Science 2022-05-13 Arthur Aubret , Céline Teulière , Jochen Triesch

Inspired by the remarkable ability of the infant visual learning system, a recent study collected first-person images from children to analyze the `training data' that they receive. We conduct a follow-up study that investigates two…

Computer Vision and Pattern Recognition · Computer Science 2019-06-05 Satoshi Tsutsui , Dian Zhi , Md Alimoor Reza , David Crandall , Chen Yu

One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own. To tackle this…

Artificial Intelligence · Computer Science 2021-01-28 Kwanyoung Park , Junseok Park , Hyunseok Oh , Byoung-Tak Zhang , Youngki Lee

Infants' ability to recognize and categorize objects develops gradually. The second year of life is marked by both the emergence of more semantic visual representations and a better understanding of word meaning. This suggests that language…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Timothy Schaumlöffel , Arthur Aubret , Gemma Roig , Jochen Triesch

In this thesis we address two related aspects of visual object recognition: the use of motion information, and the use of internal supervision, to help unsupervised learning. These two aspects are inter-related in the current study, since…

Computer Vision and Pattern Recognition · Computer Science 2018-12-14 Daniel Harari

Young children develop sophisticated internal models of the world based on their visual experience. Can such models be learned from a child's visual experience without strong inductive biases? To investigate this, we train state-of-the-art…

Computer Vision and Pattern Recognition · Computer Science 2023-09-25 A. Emin Orhan , Brenden M. Lake

Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear. Importantly, the visual system only processes the center of its field of view with high…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Timothy Schaumlöffel , Arthur Aubret , Gemma Roig , Jochen Triesch

Object concepts play a foundational role in human visual cognition, enabling perception, memory, and interaction in the physical world. Inspired by findings in developmental neuroscience - where infants are shown to acquire object…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Haoqian Liang , Xiaohui Wang , Zhichao Li , Ya Yang , Naiyan Wang

Current artificial learning systems can recognize thousands of visual categories, or play Go at a champion"s level, but cannot explain infants learning, in particular the ability to learn complex concepts without guidance, in a specific…

Neurons and Cognition · Quantitative Biology 2020-06-23 Shimon Ullman , Nimrod Dorfman , Daniel Harari

Gaze behaviors such as eye-contact or shared attention are important markers for diagnosing developmental disorders in children. While previous studies have looked at some of these elements, the analysis is usually performed on private…

Computer Vision and Pattern Recognition · Computer Science 2023-07-06 Samy Tafasca , Anshul Gupta , Jean-Marc Odobez

Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner sees the same object…

Computer Vision and Pattern Recognition · Computer Science 2023-06-01 Deepayan Sanyal , Joel Michelson , Yuan Yang , James Ainooson , Maithilee Kunda

Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed first-person videos of young children's visual experience at…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Jane Yang , Tarun Sepuri , Alvin Wei Ming Tan , Khai Loong Aw , Michael C. Frank , Bria Long

Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences. Researchers in cognitive science and developmental psychology have built formal models that…

Computer Vision and Pattern Recognition · Computer Science 2020-06-05 Satoshi Tsutsui , Arjun Chandrasekaran , Md Alimoor Reza , David Crandall , Chen Yu

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation.…

Computer Vision and Pattern Recognition · Computer Science 2017-04-13 Deepak Pathak , Ross Girshick , Piotr Dollár , Trevor Darrell , Bharath Hariharan

This paper presents an unsupervised approach towards automatically extracting video-based guidance on object usage, from egocentric video and wearable gaze tracking, collected from multiple users while performing tasks. The approach i)…

Computer Vision and Pattern Recognition · Computer Science 2016-03-22 Dima Damen , Teesid Leelasawassuk , Walterio Mayol-Cuevas

Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly…

Computer Vision and Pattern Recognition · Computer Science 2021-04-01 Silvia Bucci , Antonio D'Innocente , Yujun Liao , Fabio Maria Carlucci , Barbara Caputo , Tatiana Tommasi

Intuitive observations show that a baby may inherently possess the capability of recognizing a new visual concept (e.g., chair, dog) by learning from only very few positive instances taught by parent(s) or others, and this recognition…

Computer Vision and Pattern Recognition · Computer Science 2015-05-05 Xiaodan Liang , Si Liu , Yunchao Wei , Luoqi Liu , Liang Lin , Shuicheng Yan

Object segmentation in infant's egocentric videos is a fundamental step in studying how children perceive objects in early stages of development. From the computer vision perspective, object segmentation in such videos pose quite a few…

Computer Vision and Pattern Recognition · Computer Science 2016-02-09 Qazaleh Mirsharif , Sidharth Sadani , Shishir Shah , Hanako Yoshida , Joseph Burling

Infants develop complex visual understanding rapidly, even preceding the acquisition of linguistic skills. As computer vision seeks to replicate the human vision system, understanding infant visual development may offer valuable insights.…

Computer Vision and Pattern Recognition · Computer Science 2025-06-16 Xueyi Ke , Satoshi Tsutsui , Yayun Zhang , Bihan Wen

Human infants learn the names of objects and develop their own conceptual systems without explicit supervision. In this study, we propose methods for learning aligned vision-language conceptual systems inspired by infants' word learning…

Computer Vision and Pattern Recognition · Computer Science 2022-08-04 Taehyeong Kim , Hyeonseop Song , Byoung-Tak Zhang
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