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Found 6 papers, 4 papers with code
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, achieving significant success.
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
Event cameras offer promising advantages such as high dynamic range and low latency, making them well-suited for challenging lighting conditions and fast-moving scenarios.
Reinforcement Learning with Generalizable Gaussian Splatting
The quality of the environment representation directly influences the achievement of the learning task.
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation
Experiments show that our model can deliver better geometries, view consistencies, and rendering quality than all counterparts and benchmarks on three datasets in both generalization and finetuning settings, preliminarily proving the potential of the new paradigm for generalizable NeRF.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
