A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
IEEE Robotics and Automation Letters 7, 4829–4836
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UNVERDICTED 3representative citing papers
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.
citing papers explorer
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Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
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Topology-Driven Anti-Entanglement Control for Soft Robots
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.