2 citations · 4 across the 19 of their papers we have counts for
20 papers
Can We Perform Online RL for Image Editing without Editing Rewards?
Qichao Ma, Jikang Cheng, Ling Liang +3
Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due to costly triplet supervision…
SpikingMOT: A Spike-Driven Multi-Object Tracker
Yiding Sun, Xiangyang Yang, Dongxu Zhang +7
Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory prediction is essential for reliable target association under complex motion pa…
Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras
Ziyan Liu, Qi Su, Lulu Tang +2
Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynam…
HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking
Yao Deng, Xian Zhong, Wenxuan Liu +3
RGB cameras excel at capturing rich texture details with high spatial resolution, whereas event cameras offer exceptional temporal resolution and a high dynamic range (HDR). Levera…
: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
Kang Chen, Zhihao Liu, Tonghe Zhang +11
Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to…
SpikeGrasp: A Benchmark for 6-DoF Grasp Pose Detection from Stereo Spike Streams
Zhuoheng Gao, Jiyao Zhang, Zhiyong Xie +5
Most robotic grasping systems rely on converting sensor data into explicit 3D point clouds, which is a computational step not found in biological intelligence. This paper explores…
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
Zhuoheng Gao, Yihao Li, Jiyao Zhang +7
Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microse…
SPKLIP: Aligning Spike Video Streams with Natural Language
Yongchang Gao, Meiling Jin, Zhaofei Yu +2
Spike cameras offer unique sensing capabilities but their sparse, asynchronous output challenges semantic understanding, especially for Spike Video-Language Alignment (Spike-VLA) w…
SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos
Wenxuan Liu, Yao Deng, Kang Chen +3
Existing saliency detection methods struggle in real-world scenarios due to motion blur and occlusions. In contrast, spike cameras, with their high temporal resolution, significant…
SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams
Hanwen Liang, Xian Zhong, Wenxuan Liu +4
Restoring clear frames from rainy videos presents a significant challenge due to the rapid motion of rain streaks. Traditional frame-based visual sensors, which capture scene conte…
Rethinking High-speed Image Reconstruction Framework with Spike Camera
Kang Chen, Yajing Zheng, Tiejun Huang +1
Spike cameras, as innovative neuromorphic devices, generate continuous spike streams to capture high-speed scenes with lower bandwidth and higher dynamic range than traditional RGB…
USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting
Kang Chen, Jiyuan Zhang, Zecheng Hao +3
Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task via Neural Radi…
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture
Qichao Ma, Rui-Jie Zhu, Peiye Liu +8
Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises co…
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
Xian Zhong, Shengwang Hu, Wenxuan Liu +4
Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information proc…
SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion
Jiyuan Zhang, Kang Chen, Shiyan Chen +3
Novel View Synthesis plays a crucial role by generating new 2D renderings from multi-view images of 3D scenes. However, capturing high-speed scenes with conventional cameras often…
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
Baoyue Zhang, Yajing Zheng, Shiyan Chen +4
The amplification of high-speed micro-motions holds significant promise, with applications spanning fault detection in fast-paced industrial environments to refining precision in m…
Robust Stable Spiking Neural Networks
Jianhao Ding, Zhiyu Pan, Yujia Liu +2
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking suffi…
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Yujia Liu, Tong Bu, Jianhao Ding +3
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artific…
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
Kang Chen, Shiyan Chen, Jiyuan Zhang +4
Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited t…
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model
Zecheng Hao, Xinyu Shi, Yujia Liu +2
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in…