28 citations · 70 across the 18 of their papers we have counts for
21 papers
Beyond Source Prompts: Towards Visually Grounded AI-Generated Video Quality Assessment
Kehao Zhou, Yixuan Li, Yuxuan Jiang +1
Recent advances in generative artificial intelligence have accelerated video generation technologies. However, AI-generated videos (AIGVs) remain susceptible to various distortions…
High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning
Siyue Teng, Ho Man Kwan, Yuxuan Jiang +2
Learning-based video compression has recently achieved competitive rate-distortion performance compared to conventional video codecs. However, most existing methods rely on non-inv…
The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Jiatong Li, Zheng Chen, Kai Liu +91
This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…
The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview
Zheng Chen, Kai Liu, Jingkai Wang +150
This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…
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-…
Q-Tacit: Image Quality Assessment via Latent Visual Reasoning
Yuxuan Jiang, Yixuan Li, Hanwei Zhu +3
Vision-Language Model (VLM)-based image quality assessment (IQA) has been significantly advanced by incorporating Chain-of-Thought (CoT) reasoning. Recent work has refined image qu…
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…
GFix: Perceptually Enhanced Gaussian Splatting Video Compression
Siyue Teng, Ge Gao, Duolikun Danier +5
3D Gaussian Splatting (3DGS) enhances 3D scene reconstruction through explicit representation and fast rendering, demonstrating potential benefits for various low-level vision task…
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…
Instance Data Condensation for Image Super-Resolution
Tianhao Peng, Ho Man Kwan, Yuxuan Jiang +5
Deep learning based Image Super-Resolution (ISR) relies on large training datasets to optimize model generalization; this requires substantial computational and storage resources d…
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…
Blind Video Super-Resolution based on Implicit Kernels
Qiang Zhu, Yuxuan Jiang, Shuyuan Zhu +3
Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Exis…
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
Yuxuan Jiang, Ho Man Kwan, Tianhao Peng +5
Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (I…
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
Yuxuan Jiang, Jakub Nawała, Chen Feng +4
Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been emplo…
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
Siyue Teng, Yuxuan Jiang, Ge Gao +4
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of t…
BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
Jakub Nawała, Yuxuan Jiang, Fan Zhang +3
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and represen…
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Yuxuan Jiang, Chen Feng, Fan Zhang +1
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performa…
Compressing Deep Image Super-resolution Models
Yuxuan Jiang, Jakub Nawala, Fan Zhang +1
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance. Existing techniques…