Lighting-Aware Representation Learning under Controllable Lighting Variation

• URL: http://arxiv.org/abs/2606.06899v1
• Date: Fri, 05 Jun 2026 04:31:32 GMT
• Title: Lighting-Aware Representation Learning under Controllable Lighting Variation
• Abstract summary: We propose a lighting-aware representation learning framework that incorporates illumination variation as an explicit training signal.<n>Our method extends contrastive learning by introducing an auxiliary objective that captures illumination-dependent variation in rendered scenes.<n>Results demonstrate that the proposed lighting-aware training consistently improves downstream performance over standard contrastive learning baselines.
• Score: 2.616153119098843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and within environments. While existing approaches typically address this issue through data augmentations that encourage models to become invariant to lighting changes, such strategies do not explicitly model lighting information during learning. Inspired by theories of human vision, we propose a lighting-aware representation learning framework that incorporates illumination variation as an explicit training signal rather than a nuisance factor to be suppressed. Our method extends contrastive learning by introducing an auxiliary objective that captures illumination-dependent variation in rendered scenes, enabling the model to jointly learn representations that preserve semantic consistency while remaining sensitive to lighting-dependent visual structure. We evaluate the proposed model on image classification and object detection tasks across the ImageNet, ExDark, and PASCAL VOC benchmarks. Results demonstrate that the proposed lighting-aware training consistently improves downstream performance over standard contrastive learning baselines, while maintaining the same architecture and training budget. Furthermore, our approach shows promising performance in supervised learning frameworks and under settings involving simpler lighting variation, suggesting broad applicability beyond complex illumination scenarios. These results indicate its potential to enhance model robustness and adaptability in complex visual environments as well as in more conventional image processing tasks.

Related papers

• LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement [65.06462316546806]
  Low-light image enhancement aims to improve illumination while preserving high-quality color and texture.<n>Existing methods often fail to extract reliable feature representations due to severely degraded pixel-level information under low-light conditions.<n>We propose LightQANet, a novel framework that introduces quantized and adaptive feature learning for low-light enhancement.
  arXiv  Detail & Related papers  (2025-10-16T14:54:42Z)
• PractiLight: Practical Light Control Using Foundational Diffusion Models [78.75949075070595]
  PractiLight is a practical approach to light control in generated images.<n>Our key insight is that lighting relationships in an image are similar in nature to token interaction in self-attention layers.<n>We demonstrate state-of-the-art performance in terms of quality and control with proven parameter and data efficiency.
  arXiv  Detail & Related papers  (2025-09-01T23:38:40Z)
• SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement [58.79901582809091]
  Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>We present a Spatially-Adaptive Illumination-Guided Transformer framework that enables accurate illumination restoration.
  arXiv  Detail & Related papers  (2025-07-21T11:38:56Z)
• Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models [56.84206059390887]
  We propose textbfLightD, a novel framework that generates natural adversarial samples for vision-and-language pretraining models.<n>LightD expands the optimization space while ensuring perturbations align with scene semantics.
  arXiv  Detail & Related papers  (2025-05-30T05:30:02Z)
• Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement [3.174882428337821]
  We propose a variational method for low-light image enhancement based on the Retinex decomposition.<n>A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition.<n>We extend the model by introducing its deep unfolding counterpart, in which the operators are replaced with learnable networks.
  arXiv  Detail & Related papers  (2025-04-10T14:48:26Z)
• TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement [30.498816319802412]
  We propose a new light enhancement task and a new framework that provides customized lighting control through prompt-driven, semantic-level, and quantitative brightness adjustments.<n> Experimental results on benchmark datasets demonstrate our framework's superior performance at increasing visibility, maintaining natural color balance, and amplifying fine details without creating artifacts.
  arXiv  Detail & Related papers  (2025-03-11T08:30:50Z)
• ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement [10.957431540794836]
  Inadequate illumination can lead to significant information loss and poor image quality, impacting various applications such as surveillance.<n>Current enhancement techniques often use specific datasets to enhance low-light images, but still present challenges when adapting to diverse real-world conditions.<n>The Adaptive Light Enhancement Network (ALEN) is introduced, whose main approach is the use of a classification mechanism to determine whether local or global illumination enhancement is required.
  arXiv  Detail & Related papers  (2024-07-29T05:19:23Z)
• CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement [97.95330185793358]
  Low-light image enhancement (LLIE) aims to improve low-illumination images.<n>Existing methods face two challenges: uncertainty in restoration from diverse brightness degradations and loss of texture and color information.<n>We propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement.
  arXiv  Detail & Related papers  (2024-04-08T07:34:39Z)
• Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation [36.050270650417325]
  We propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework.
  arXiv  Detail & Related papers  (2022-10-07T19:32:55Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

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.