354 citations · 357 across the 12 of their papers we have counts for
12 papers
A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding
Shuai Wang, Hongyi Zhu, Jia-Hong Huang +6
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise…
The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results
Jingkai Wang, Jue Gong, Zheng Chen +50
This paper provides a review of the NTIRE 2026 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on ge…
NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results
Xin Li, Jiachao Gong, Xijun Wang +75
This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…
SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation
Chengxi Zeng, Yuxuan Jiang, Ge Gao +6
Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ende…
EfficientSAM3: Progressive Hierarchical Distillation for Video Concept Segmentation from SAM1, 2, and 3
Chengxi Zeng, Yuxuan Jiang, Aaron Zhang
The Segment Anything Model 3 (SAM3) advances visual understanding with Promptable Concept Segmentation (PCS) across images and videos, but its unified architecture (shared vision b…
Compressed Video Super-Resolution based on Hierarchical Encoding
Yuxuan Jiang, Siyue Teng, Qiang Zhu +6
This paper presents a general-purpose video super-resolution (VSR) method, dubbed VSR-HE, specifically designed to enhance the perceptual quality of compressed content. Targeting s…
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation
Chengxi Zeng, Yuxuan Jiang, Fan Zhang +2
The deployment of foundation models for medical imaging has demonstrated considerable success. However, their training overheads associated with downstream tasks remain substantial…
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales
Yuxuan Jiang, Chengxi Zeng, Siyue Teng +4
In recent years, attention mechanisms have been exploited in single image super-resolution (SISR), achieving impressive reconstruction results. However, these advancements are stil…
Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations
Chengxi Zeng, David Smithard, Alberto M Gambaruto +1
Vision foundation models have demonstrated exceptional generalization capabilities in segmentation tasks for both generic and specialized images. However, a performance gap persist…
RBF-PINN: Non-Fourier Positional Embedding in Physics-Informed Neural Networks
Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto
While many recent Physics-Informed Neural Networks (PINNs) variants have had considerable success in solving Partial Differential Equations, the empirical benefits of feature mappi…
Feature Mapping in Physics-Informed Neural Networks (PINNs)
Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto
In this paper, the training dynamics of PINNs with a feature mapping layer via the limiting Conjugate Kernel and Neural Tangent Kernel is investigated, shedding light on the conver…
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
Chengxi Zeng, Xinyu Yang, David Smithard +3
This paper presents a deep learning framework for medical video segmentation. Convolution neural network (CNN) and transformer-based methods have achieved great milestones in medic…