82 citations · 182 across the 16 of their papers we have counts for
16 papers
YOLO-IOD: Towards Real Time Incremental Object Detection
Shizhou Zhang, Xueqiang Lv, Yinghui Xing +4
Current methods for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO dete…
DuGI-MAE: Improving Infrared Mask Autoencoders via Dual-Domain Guidance
Yinghui Xing, Xiaoting Su, Shizhou Zhang +2
Infrared imaging plays a critical role in low-light and adverse weather conditions. However, due to the distinct characteristics of infrared images, existing foundation models such…
On Modality Incomplete Infrared-Visible Object Detection: An Architecture Compatibility Perspective
Shuo Yang, Yinghui Xing, Shizhou Zhang +1
Infrared and visible object detection (IVOD) is essential for numerous around-the-clock applications. Despite notable advancements, current IVOD models exhibit notable performance…
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
Qirui Wu, Shizhou Zhang, De Cheng +4
Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or know…
Frequency-Guided Spatial Adaptation for Camouflaged Object Detection
Shizhou Zhang, Dexuan Kong, Yinghui Xing +5
Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that e…
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach
Shizhou Zhang, Wenlong Luo, De Cheng +4
In this paper, we construct a large-scale benchmark dataset for Ground-to-Aerial Video-based person Re-Identification, named G2A-VReID, which comprises 185,907 images and 5,576 tra…
Visual Prompt Tuning in Null Space for Continual Learning
Yue Lu, Shizhou Zhang, De Cheng +4
Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL), by selecting and updating relevant prompts in the vision-transformer models. On…
DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
Lingyan Ran, Lushuang Wang, Tao Zhuo +1
Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data. Unsupervised domain adaptation (UDA) has proven to be advan…
CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model
Yinghui Xing, Litao Qu, Shizhou Zhang +2
Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral i…
Ground-to-Aerial Person Search: Benchmark Dataset and Approach
Shizhou Zhang, Qingchun Yang, De Cheng +4
In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identit…
Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection
Yinghui Xing, Dexuan Kong, Shizhou Zhang +4
Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicat…
MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization
Yinghui Xing, Shuo Yang, Song Wang +4
Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information espec…
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Yinghui Xing, Qirui Wu, De Cheng +4
With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tu…
PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening
Yinghui Xing, Shuyuan Yang, Song Wang +2
The fusion of multispectral and panchromatic images is always dubbed pansharpening. Most of the available deep learning-based pan-sharpening methods sharpen the multispectral image…
Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
Yinghui Xing, Yan Zhang, Houjun He +2
The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as panshar…
Text-based Person Search in Full Images via Semantic-Driven Proposal Generation
Shizhou Zhang, De Cheng, Wenlong Luo +6
Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real…