1 code implementation • 30 Nov 2023 • Yudong Wang, Jichang Guo, Wanru He, Huan Gao, Huihui Yue, Zenan Zhang, Chongyi Li
Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection.
no code implementations • CVPR 2022 • Huan Gao, Jichang Guo, Guoli Wang, Qian Zhang
The invariance of illumination or inherent difference between two images is fully explored so as to make up for the lack of labels for nighttime images.
1 code implementation • 12 Mar 2021 • Yudong Wang, Jichang Guo, Huan Gao, Huihui Yue
However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation.
9 code implementations • CVPR 2020 • Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, Runmin Cong
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Ranked #1 on Color Constancy on INTEL-TUT2
1 code implementation • NeurIPS 2018 • Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang
Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions.
no code implementations • 20 Nov 2018 • Yunlong Yu, Zhong Ji, Yanwei Pang, Jichang Guo, Zhongfei Zhang, Fei Wu
Existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from the visual features to the class semantics, which fails to construct the visual-semantic interactions well.
no code implementations • 21 May 2018 • Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei Zhang
To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
no code implementations • 21 Mar 2018 • Chongyi Li, Jichang Guo, Fatih Porikli, Huazhu Fu, Yanwei Pang
Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks.
no code implementations • 26 Dec 2017 • Yunlong Yu, Zhong Ji, Jichang Guo, Zhongfei, Zhang
Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way.
no code implementations • 2 Dec 2017 • Chongyi Li, Jichang Guo, Fatih Porikli, Chunle Guo, Huzhu Fu, Xi Li
Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications.
no code implementations • 19 Oct 2017 • Chongyi Li, Jichang Guo, Chunle Guo
Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water.
no code implementations • 22 May 2017 • Zhong Ji, Yunxin Sun, Yulong Yu, Jichang Guo, Yanwei Pang
However, the visual features and the class semantic descriptors locate in different structural spaces, a linear or bilinear model can not capture the semantic interactions between different modalities well.
no code implementations • 27 Mar 2017 • Yunlong Yu, Zhong Ji, Xi Li, Jichang Guo, Zhongfei Zhang, Haibin Ling, Fei Wu
As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data.
no code implementations • 27 Mar 2017 • Yunlong Yu, Zhong Ji, Jichang Guo, Yanwei Pang
Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively.