97 citations · 128 across the 16 of their papers we have counts for
17 papers
Towards Ultrafast Depth Sensing Via Active Event-based Stereo Vision
Jianing Li, Yunjian Zhang, Haiqian Han +2
Conventional frame-based imaging for active stereo systems has encountered major challenges in fast-motion scenarios. However, how to design a novel paradigm for ultrafast depth se…
Neuromorphic Object Detection: An In-Depth Study and Future Directions
Jianing Li, Dianze Li, Arren Glover +5
Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchrono…
EventFlash: Towards Efficient MLLMs for Event-Based Vision
Shaoyu Liu, Jianing Li, Guanghui Zhao +4
Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios, addressing key limitations of frame-based MLLMs. However, curre…
Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement
Yunjian Zhang, Sudong Wang, Yang Li +5
Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards (RLVR) emerging as a principled…
Learning to Remove Lens Flare in Event Camera
Haiqian Han, Lingdong Kong, Jianing Li +7
Event cameras have the potential to revolutionize vision systems with their high temporal resolution and dynamic range, yet they remain susceptible to lens flare, a fundamental opt…
EventBench: Towards Comprehensive Benchmarking of Event-based MLLMs
Shaoyu Liu, Jianing Li, Guanghui Zhao +2
Multimodal large language models (MLLMs) have made significant advancements in event-based vision, yet the comprehensive evaluation of their capabilities within a unified benchmark…
SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition
Peiran Xu, Sudong Wang, Yao Zhu +3
Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language model…
Towards Understanding How Knowledge Evolves in Large Vision-Language Models
Sudong Wang, Yunjian Zhang, Yao Zhu +4
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications. However, understanding their internal working mechanisms h…
EventGPT: Event Stream Understanding with Multimodal Large Language Models
Shaoyu Liu, Jianing Li, Guanghui Zhao +5
Event cameras record visual information as asynchronous pixel change streams, excelling at scene perception under unsatisfactory lighting or high-dynamic conditions. Existing multi…
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
Dianze Li, Jianing Li, Xu Liu +3
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two indepen…
Event Stream-based Sign Language Translation: A High-Definition Benchmark Dataset and A Novel Baseline
Shiao Wang, Xiao Wang, Duoqing Yang +5
Sign Language Translation (SLT) is a core task in the field of AI-assisted disability. Traditional SLT methods are typically based on visible light videos, which are easily affecte…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Xu Liu, Jianing Li, Xiaopeng Fan +1
Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocula…
Temporal Up-Sampling for Asynchronous Events
Xijie Xiang, Lin Zhu, Jianing Li +2
The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid eve…
1000x Faster Camera and Machine Vision with Ordinary Devices
Tiejun Huang, Yajing Zheng, Zhaofei Yu +13
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we pres…
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
Lin Zhu, Xiao Wang, Yi Chang +3
Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called `events' with high temporal resolution…
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Xiao Wang, Jianing Li, Lin Zhu +6
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lo…