1 code implementation • 12 Nov 2023 • HaoNing Wu, ZiCheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Kaixin Xu, Chunyi Li, Jingwen Hou, Guangtao Zhai, Geng Xue, Wenxiu Sun, Qiong Yan, Weisi Lin
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model.
1 code implementation • 6 Aug 2023 • Chaofeng Chen, Jiadi Mo, Jingwen Hou, HaoNing Wu, Liang Liao, Wenxiu Sun, Qiong Yan, Weisi Lin
Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.
Ranked #11 on Video Quality Assessment on MSU SR-QA Dataset
no code implementations • 29 Jun 2023 • Weide Liu, Xiaoyang Zhong, Jingwen Hou, Shaohua Li, Haozhe Huang, Yuming Fang
Multimodal Named Entity Recognition (MNER) is a crucial task for information extraction from social media platforms such as Twitter.
1 code implementation • 22 May 2023 • HaoNing Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e. g. sharpness of a video).
2 code implementations • 28 Apr 2023 • HaoNing Wu, Liang Liao, Annan Wang, Chaofeng Chen, Jingwen Hou, Wenxiu Sun, Qiong Yan, Weisi Lin
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies.
2 code implementations • 26 Feb 2023 • HaoNing Wu, Liang Liao, Jingwen Hou, Chaofeng Chen, Erli Zhang, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions.
3 code implementations • ICCV 2023 • HaoNing Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
Ranked #1 on Video Quality Assessment on LIVE-VQC
4 code implementations • 11 Oct 2022 • HaoNing Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Jinwei Gu, Weisi Lin
On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment.
Ranked #2 on Video Quality Assessment on KoNViD-1k (using extra training data)
4 code implementations • 6 Jul 2022 • HaoNing Wu, Chaofeng Chen, Jingwen Hou, Liang Liao, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Consisting of fragments and FANet, the proposed FrAgment Sample Transformer for VQA (FAST-VQA) enables efficient end-to-end deep VQA and learns effective video-quality-related representations.
Ranked #3 on Video Quality Assessment on LIVE-VQC (using extra training data)
1 code implementation • 20 Jun 2022 • HaoNing Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Weisi Lin
Based on prominent time-series modeling ability of transformers, we propose a novel and effective transformer-based VQA method to tackle these two issues.
Ranked #5 on Video Quality Assessment on KoNViD-1k
no code implementations • 2 Jun 2022 • Jingwen Hou, Henghui Ding, Weisi Lin, Weide Liu, Yuming Fang
To deal with this dilemma, we propose to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model.