2 code implementations • ECCV 2020 • Longrong Yang, Fanman Meng, Hongliang Li, Qingbo Wu, Qishang Cheng
Specifically, in instance segmentation, noisy class labels play different roles in the foreground-background sub-task and the foreground-instance sub-task.
no code implementations • 13 May 2024 • Chenhao Wu, Qingbo Wu, Haoran Wei, Shuai Chen, Lei Wang, King Ngi Ngan, Fanman Meng, Hongliang Li
The findings from these experiments can provide a valuable reference for the development of compression algorithms with enhanced adversarial robustness.
no code implementations • 27 Dec 2023 • Hefei Mei, Taijin Zhao, Shiyuan Tang, Heqian Qiu, Lanxiao Wang, Minjian Zhang, Fanman Meng, Hongliang Li
By transferring the knowledge of IFC from the base training to fine-tuning, the IFC generates plentiful novel samples to calibrate the novel class distribution.
no code implementations • 27 Nov 2023 • Lei Wang, Qingbo Wu, Desen Yuan, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu
Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the most popular choice.
no code implementations • 10 Oct 2023 • Zhaofeng Shi, Qingbo Wu, Fanman Meng, Linfeng Xu, Hongliang Li
Firstly, a Cross-modal Cognitive Consensus Inference Module (C3IM) is developed to extract a unified-modal label by integrating audio/visual classification confidence and similarities of modality-agnostic label embeddings.
1 code implementation • 26 Jan 2023 • Linfeng Xu, Qingbo Wu, Lili Pan, Fanman Meng, Hongliang Li, Chiyuan He, Hanxin Wang, Shaoxu Cheng, Yu Dai
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
no code implementations • CVPR 2023 • Benliu Qiu, Hongliang Li, Haitao Wen, Heqian Qiu, Lanxiao Wang, Fanman Meng, Qingbo Wu, Lili Pan
We place continual learning into a causal framework, based on which we find the task-induced bias is reduced naturally by two underlying mechanisms in task and domain incremental learning.
no code implementations • CVPR 2023 • Chao Shang, Hongliang Li, Fanman Meng, Qingbo Wu, Heqian Qiu, Lanxiao Wang
Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes.
1 code implementation • 15 Sep 2022 • Rui Ma, Qingbo Wu, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu
More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other.
no code implementations • 16 Jun 2022 • Heqian Qiu, Hongliang Li, Taijin Zhao, Lanxiao Wang, Qingbo Wu, Fanman Meng
Unfortunately, there is no effort to explore crowd understanding in multi-modal domain that bridges natural language and computer vision.
1 code implementation • 5 Apr 2021 • Haoran Wei, Qingbo Wu, Hui Li, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu
In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.
1 code implementation • 21 Jul 2020 • Jiasong Wu, Xuan Li, Taotao Li, Fanman Meng, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu
We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem.
no code implementations • 14 Oct 2019 • Yuwei Yang, Fanman Meng, Hongliang Li, Qingbo Wu, Xiaolong Xu, Shuai Chen
The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks.
Ranked #79 on Few-Shot Semantic Segmentation on PASCAL-5i (5-Shot)
no code implementations • 26 Sep 2019 • Qingbo Wu, Lei Wang, King N. Ngan, Hongliang Li, Fanman Meng, Linfeng Xu
Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images.
no code implementations • 21 Sep 2019 • Kaixu Huang, Fanman Meng, Hongliang Li, Shuai Chen, Qingbo Wu, King N. Ngan
Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary.
no code implementations • 19 Sep 2019 • Yuwei Yang, Fanman Meng, Hongliang Li, King N. Ngan, Qingbo Wu
This paper studies few-shot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already existed.
no code implementations • 23 Jan 2019 • Fanman Meng, Kaixu Huang, Hongliang Li, Qingbo Wu
Existing method generates class activation map (CAM) by a set of fixed classes (i. e., using all the classes), while the discriminative cues between class pairs are not considered.
no code implementations • 10 Jan 2019 • Lei Ma, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan
Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture.
no code implementations • ECCV 2018 • Hengcan Shi, Hongliang Li, Fanman Meng, Qingbo Wu
On the other hand, the relationships of different image regions are not considered as well, even though they are greatly important to eliminate the undesired foreground object in accordance with specific query.
no code implementations • 15 May 2017 • Qingbo Wu, Hongliang Li, Fanman Meng, King N. Ngan
By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient.
no code implementations • 3 Feb 2015 • Hongyuan Zhu, Fanman Meng, Jianfei Cai, Shijian Lu
Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision.