activity
20192026
most citedA Learning Framework for n-bit Quantized Neural Networks toward FPGAs

34 citations · 40 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Low Light Image Enhancement Challenge at NTIRE 2026

George Ciubotariu, Sharif S M A, Abdur Rehman +90

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…

cs.CV2026

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…

cs.CV2026

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)

Ya-nan Guan, Shaonan Zhang, Hang Guo +55

In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite sign…

cs.CV2020

APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment

Jiangning Zhang, Xianfang Zeng, Chao Xu +3

Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special pe…

cs.CV20206 cited

Semantic Graph Based Place Recognition for 3D Point Clouds

Xin Kong, Xuemeng Yang, Guangyao Zhai +6

Due to the difficulty in generating the effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlik…

cs.LG202034 cited

A Learning Framework for n-bit Quantized Neural Networks toward FPGAs

Jun Chen, Liang Liu, Yong Liu +1

The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of FPGAs. This paper proposes a novel learning fram…