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Found 8 papers, 3 papers with code
Distractor-aware Event-based Tracking
We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.
In the Blink of an Eye: Event-based Emotion Recognition
To the best of our knowledge, our method is the first eye-based emotion recognition method that leverages event-based cameras and spiking neural network.
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
The alignment module is responsible for cross-style and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events.
Spiking Transformers for Event-Based Single Object Tracking
In particular, the proposed architecture features a transformer module to provide global spatial information and a spiking neural network (SNN) module for extracting temporal cues.
Object Tracking by Jointly Exploiting Frame and Event Domain
Our approach's effectiveness is enforced by a novel designed cross-domain attention schemes, which can effectively enhance features based on self- and cross-domain attention schemes; The adaptiveness is guarded by a specially designed weighting scheme, which can adaptively balance the contribution of the two domains.
Ranked #3 on
Object Tracking
on FE108
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking.
A Two-Stage Attentive Network for Single Image Super-Resolution
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress.
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs).
