120 citations · 232 across the 9 of their papers we have counts for
9 papers
Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection
Hao Wang, Jiqing Zhang, Xin Yang +4
Camouflaged Object Detection (COD) aims to segment objects that blend seamlessly into complex backgrounds, with growing interest in exploiting additional visual modalities to enhan…
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
Yimeng Shan, Zhenbang Ren, Haodi Wu +11
Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through di…
Distractor-aware Event-based Tracking
Yingkai Fu, Meng Li, Wenxi Liu +5
Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture…
In the Blink of an Eye: Event-based Emotion Recognition
Haiwei Zhang, Jiqing Zhang, Bo Dong +5
We introduce a wearable single-eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in…
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
Jiqing Zhang, Yuanchen Wang, Wenxi Liu +4
Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially f…
Object Tracking by Jointly Exploiting Frame and Event Domain
Jiqing Zhang, Xin Yang, Yingkai Fu +3
Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- a…
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jiqing Zhang, Kai Zhao, Bo Dong +4
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effective…
A Two-Stage Attentive Network for Single Image Super-Resolution
Jiqing Zhang, Chengjiang Long, Yuxin Wang +4
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the exist…
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Xin Yang, Haiyang Mei, Jiqing Zhang +4
Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a p…