Self-Supervised Learning of Color Constancy

• URL: http://arxiv.org/abs/2404.08127v1
• Date: Thu, 11 Apr 2024 21:07:38 GMT
• Title: Self-Supervised Learning of Color Constancy
• Authors: Markus R. Ernst, Francisco M. López, Arthur Aubret, Roland W. Fleming, Jochen Triesch,
• Abstract summary: Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. We present a first study showing that CC develops in a neural network trained in a self-supervised manner.
• Score: 2.8411923586195478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its limitations have been carefully characterized in humans, it is still unclear how the visual system acquires this ability during development. Here, we present a first study showing that CC develops in a neural network trained in a self-supervised manner through an invariance learning objective. During learning, objects are presented under changing illuminations, while the network aims to map subsequent views of the same object onto close-by latent representations. This gives rise to representations that are largely invariant to the illumination conditions, offering a plausible example of how CC could emerge during human cognitive development via a form of self-supervised learning.

Related papers

• Probing Perceptual Constancy in Large Vision Language Models [8.826002715344911]
  We evaluated 33 Vision-Language Models (VLMs) using 253 experiments across three domains: color, size, and shape constancy. We found significant variability in VLM performance, with models performance in shape constancy clearly dissociated from that of color and size constancy.
  arXiv  Detail & Related papers  (2025-02-14T16:31:43Z)
• Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior [88.9319150230121]
  Object-centric vision aims to construct an explicit representation of the objects in a scene. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models. We obtain significant improvements in segmenting objects in both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-31T04:35:50Z)
• A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play [3.486683381782259]
  We study how learning signals that equate different viewpoints can support robust visual learning. We find that representations learned by equating different physical viewpoints of an object benefit downstream image classification accuracy.
  arXiv  Detail & Related papers  (2023-05-30T22:42:03Z)
• Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification [90.39454748065558]
  Body shape is one of the significant modality-shared cues for VI-ReID. We propose shape-erased feature learning paradigm that decorrelates modality-shared features in two subspaces. Experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-04-09T10:22:10Z)
• Self-Supervised Learning for Place Representation Generalization across Appearance Changes [11.030196234282675]
  We investigate learning features that are robust to appearance modifications while sensitive to geometric transformations in a self-supervised manner. Our results reveal that jointly learning appearance-robust and geometry-sensitive image descriptors leads to competitive visual place recognition results.
  arXiv  Detail & Related papers  (2023-03-04T10:14:47Z)
• Robust and Controllable Object-Centric Learning through Energy-based Models [95.68748828339059]
  ours is a conceptually simple and general approach to learning object-centric representations through an energy-based model. We show that ours can be easily integrated into existing architectures and can effectively extract high-quality object-centric representations.
  arXiv  Detail & Related papers  (2022-10-11T15:11:15Z)
• On Binding Objects to Symbols: Learning Physical Concepts to Understand Real from Fake [155.6741526791004]
  We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate synthetic data. We characterize physical objects as abstract concepts and use the previous analysis to show that physical objects can be encoded by finite architectures. We conclude that binding physical entities to digital identities is possible in finite time with finite resources.
  arXiv  Detail & Related papers  (2022-07-25T17:21:59Z)
• Embodied vision for learning object representations [4.211128681972148]
  We show that visual statistics mimicking those of a toddler improve object recognition accuracy in both familiar and novel environments. We argue that this effect is caused by the reduction of features extracted in the background, a neural network bias for large features in the image and a greater similarity between novel and familiar background regions.
  arXiv  Detail & Related papers  (2022-05-12T16:36:27Z)
• Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams [64.82800502603138]
  This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that allows the agent to learn by observing what is moving in the attended locations. Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
  arXiv  Detail & Related papers  (2022-04-26T09:52:31Z)
• Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation [53.62256887837659]
  Kinship verification is a long-standing research challenge in computer vision. We propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties. Our DCML method is always superior to some state-of-the-art kinship verification methods.
  arXiv  Detail & Related papers  (2021-09-07T01:34:51Z)
• Visualizing and Understanding Vision System [0.6510507449705342]
  We use a vision recognition-reconstruction network (RRN) to investigate the development, recognition, learning and forgetting mechanisms. In digit recognition study, we witness that the RRN could maintain object invariance representation under various viewing conditions. In the learning and forgetting study, novel structure recognition is implemented by adjusting entire synapses in low magnitude while pattern specificities of original synaptic connectivity are preserved.
  arXiv  Detail & Related papers  (2020-06-11T07:08:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.