LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception

arXiv:2512.08912 · cs.CV, cs.RO · Submitted 2025-12-09 · Read on arXiv

cs.CV, cs.RO

Submitted: 2025-12-09

Updated: 2026-09-22

Terminology

• Generalization — 24× in this paper · explained in 13 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Robustness — 8× in this paper · explained in 5 episodes
• Domain Adaptation — 7× in this paper · explained in 5 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Downstream Tasks — 5× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Active Vision — 2× in this paper · explained in 1 episode
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Active Perception — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey
• Decoupled Weight Decay Regularization
• LED: Light Enhanced Depth Estimation at Night
• Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models