10 citations · 16 across the 9 of their papers we have counts for
9 papers
GTR: Gated Token Recurrence for Efficient Dense Prediction
Zhe Feng, Longfei Liu, Wei Liu +7
Self-attention-based vision backbones perform well on dense prediction, but the quadratic computational cost of global softmax attention limits their efficiency as image resolution…
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps
Xuanlong Yu, Youyang Sha, Longfei Liu +2
Few-shot object detection (FSOD) is challenging due to unstable optimization and limited generalization arising from the scarcity of training samples. To address these issues, we p…
OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation
Leilei Wang, Longfei Liu, Xi Shen +4
Real-time open-vocabulary object detection (OVOD) is essential for practical deployment in dynamic environments, where models must recognize a large and evolving set of categories…
EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
Longfei Liu, Yongjie Hou, Yang Li +7
Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory budgets. In practice, lightweight s…
FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusion
Chen-Bin Feng, Youyang Sha, Longfei Liu +4
In this paper, we present FSOD-VFM: Few-Shot Object Detectors with Vision Foundation Models, a framework that leverages vision foundation models to tackle the challenge of few-shot…
Real-Time Object Detection Meets DINOv3
Shihua Huang, Yongjie Hou, Longfei Liu +2
Driven by the simple and effective Dense O2O, DEIM demonstrates faster convergence and enhanced performance. In this work, we extend it with DINOv3 features, resulting in DEIMv2. D…
From COCO to COCO-FP: A Deep Dive into Background False Positives for COCO Detectors
Longfei Liu, Wen Guo, Shihua Huang +2
Reducing false positives is essential for enhancing object detector performance, as reflected in the mean Average Precision (mAP) metric. Although object detectors have achieved no…
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Longfei Liu, Sheng Li, Yisong Chen +1
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing. We present a new method, called X-GANs, for reconstruction…