1 citations · 1 across the 9 of their papers we have counts for
9 papers
LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models
Soumyaratna Debnath, Bui Duc Manh, Zinan Liu +1
Vision-Language Models (VLMs) typically assume a uniform spatial fidelity across the entire field of view of visual inputs, dedicating equal precision to even the uninformative reg…
Reference-Free Omnidirectional Stereo Matching via Multi-View Consistency Maximization
Lehuai Xu, Weiming Zhang, Yang Li +2
Reliable omnidirectional depth estimation from multi-fisheye stereo matching is pivotal to many applications, such as embodied robotics. Existing approaches either rely on spherica…
ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events
Kanghao Chen, Zeyu Wang, Lin Wang
Recent years have witnessed tremendous progress in the 3D reconstruction of dynamic humans from a monocular video with the advent of neural rendering techniques. This task has a wi…
Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors
Zixin Zhang, Kanghao Chen, Lin Wang
Event cameras are bio-inspired sensors that output asynchronous and sparse event streams, instead of fixed frames. Benefiting from their distinct advantages, such as high dynamic r…
PASS: Path-selective State Space Model for Event-based Recognition
Jiazhou Zhou, Kanghao Chen, Lei Zhang +1
Event cameras are bio-inspired sensors that capture intensity changes asynchronously with distinct advantages, such as high temporal resolution. Existing methods for event-based ob…
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More
Kanghao Chen, Guoqiang Liang, Hangyu Li +2
Event cameras offer significant advantages for low-light video enhancement, primarily due to their high dynamic range. Current research, however, is severely limited by the absence…
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations
Zipeng Wang, Yunfan Lu, Lin Wang
Reconstructing intensity frames from event data while maintaining high temporal resolution and dynamic range is crucial for bridging the gap between event-based and frame-based com…
LaSe-E2V: Towards Language-guided Semantic-Aware Event-to-Video Reconstruction
Kanghao Chen, Hangyu Li, JiaZhou Zhou +2
Event cameras harness advantages such as low latency, high temporal resolution, and high dynamic range (HDR), compared to standard cameras. Due to the distinct imaging paradigm shi…
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
Guoqiang Liang, Kanghao Chen, Hangyu Li +2
Event camera has recently received much attention for low-light image enhancement (LIE) thanks to their distinct advantages, such as high dynamic range. However, current research i…