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Found 19 papers, 6 papers with code
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature.
Reinforcement Learning with Generalizable Gaussian Splatting
The quality of the environment representation directly influences the achievement of the learning task.
Spiking Wavelet Transformer
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
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.
Decode Neural signal as Speech
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of vision and language.
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.
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs).
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds.
Pursing the Sparse Limitation of Spiking Deep Learning Structures
It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.
Fully Spiking Neural Network for Legged Robots
In recent years, legged robots based on deep reinforcement learning have made remarkable progress.
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications.
3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
Accurate representation of tooth position is extremely important in treatment.
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.
TTPOINT: A Tensorized Point Cloud Network for Lightweight Action Recognition with Event Cameras
In the experiment, TTPOINT emerged as the SOTA method on three datasets while also attaining SOTA among point cloud methods on all five datasets.
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models
While Large Language Models (LLMs) have demonstrated remarkable potential in natural language generation and instruction following, a persistent challenge lies in their susceptibility to "hallucinations", which erodes trust in their outputs.
Spiking Denoising Diffusion Probabilistic Models
In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality.
Masked Spiking Transformer
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature.
