6 papers
Noisy Spiking Actor Network for Exploration
Ding Chen, Peixi Peng, Tiejun Huang +1
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies. Spiking neural networks (SNNs), due to their…
Delta-Triplane Transformers as Occupancy World Models
Haoran Xu, Peixi Peng, Guang Tan +3
Occupancy World Models (OWMs) aim to predict future scenes via 3D voxelized representations of the environment to support intelligent motion planning. Existing approaches typically…
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Dong Xiao, Guangyao Chen, Peixi Peng +4
Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is c…
CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection
Mingyi Guo, Yuyang Liu, Zhiyuan Yan +3
Incremental object detection is fundamentally challenged by catastrophic forgetting. A major factor contributing to this issue is background shift, where background categories in s…
Solving the Catastrophic Forgetting Problem in Generalized Category Discovery
Xinzi Cao, Xiawu Zheng, Guanhong Wang +5
Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets, providing a more realistic setting for image recognition. Esse…
Sensitivity Decouple Learning for Image Compression Artifacts Reduction
Li Ma, Yifan Zhao, Peixi Peng +1
With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevai…