Search Results for author:
Found 8 papers, 6 papers with code
Contrastive Initial State Buffer for Reinforcement Learning
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples.
Seeing Behind Dynamic Occlusions with Event Cameras
We present the first large-scale dataset consisting of synchronized images and event sequences to evaluate our approach.
Revisiting Token Pruning for Object Detection and Instance Segmentation
In relation to the dense counterpart that utilizes all tokens, our method realizes an increase in inference speed, achieving up to 34% faster performance for the entire network and 46% for the backbone.
Data-driven Feature Tracking for Event Cameras
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios.
ESS: Learning Event-based Semantic Segmentation from Still Images
Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the lack of high-quality, labeled datasets.
Ranked #5 on
Event-based Object Segmentation
on MVSEC-SEG
Multi-Bracket High Dynamic Range Imaging with Event Cameras
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times.
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
However, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras.
Event-based Asynchronous Sparse Convolutional Networks
However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency.
