VCD: View-Constraint Disentanglement for Action Recognition

Zhong, Xian; Zhou, Zhuo; Liu, Wenxuan; Jiang, Kui; Jia, Xuemei; Huang, Wenxin; Wang, Zheng

doi:10.1109/icassp43922.2022.9747610

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The results in Fig. 5 show that the proposed biological features can outperform those deep features extracted from the CNN models in temporal quality perception, which motivate us to address the related vision problems, such as image restoration [21,22], action recognition [62,63], and video compression [52,53] considering the characteristics of the HVS.…”

Section: Ablation Studymentioning

confidence: 92%

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

Liao¹,

Xu²,

Wu³

et al. 2022

Preprint

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With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts have been made to solve this problem, it remains unclear how the human visual system (HVS) relates to the temporal quality of videos. Meanwhile, recent work has found that the frames of natural video transformed into the perceptual domain of the HVS tend to form a straight trajectory of the representations. With the obtained insight that distortion impairs the perceived video quality and results in a curved trajectory of the perceptual representation, we propose a temporal perceptual quality index (TPQI) to measure the temporal distortion by describing the graphic morphology of the representation. Specifically, we first extract the video perceptual representations from the lateral geniculate nucleus (LGN) and primary visual area (V1) of the HVS, and then measure the straightness and compactness of their trajectories to quantify the degradation in naturalness and content continuity of video. Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations. We further demonstrate that by combining with NIQE, a spatial quality metric, TPQI can achieve top performance over popular in-the-wild video datasets. More importantly, TPQI does not require any additional information beyond the video being evaluated and thus can be applied to any datasets without parameter tuning. Source code is available at https://github.com/UoLMM/TPQI-VQA.

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Section: Ablation Studymentioning

confidence: 92%

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

Liao¹,

Xu²,

Wu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These methods show that the structural prior effectively helps to improve the quality of the completed images. In spite of this, they still struggle in cases when holes are (d) EC [11] (e) GI [14] (f) Ours To seek more prior information to help solve this type of ill-posed problems, reference images with similar textures and structures are introduced in some vision tasks, such as image super-resolution [15,16], image compression [17] and action recognition [23]. Their motivation is to utilize rich textures from the references to compensate for the lost details in the input images and thereby produce more detailed and realistic content.…”

Section: Introductionmentioning

confidence: 99%

Reference-Guided Texture and Structure Inference for Image Inpainting

Liu

Liao

Wang

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Existing learning-based image inpainting methods are still in challenge when facing complex semantic environments and diverse hole patterns. The prior information learned from the large scale training data is still insufficient for these situations. Reference images captured covering the same scenes share similar texture and structure priors with the corrupted images, which offers new prospects for the image inpainting tasks. Inspired by this, we first build a benchmark dataset containing 10K pairs of input and reference images for reference-guided inpainting. Then we adopt an encoderdecoder structure to separately infer the texture and structure features of the input image considering their pattern discrepancy of texture and structure during inpainting. A feature alignment module is further designed to refine these features of the input image with the guidance of a reference image. Both quantitative and qualitative evaluations demonstrate the superiority of our method over the state-of-the-art methods in terms of completing complex holes. Code is available at https://github.com/Cameltr/RGTSI.

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Background-Weakening Consistency Regularization for Semi-Supervised Video Action Detection

Zhong

Liu

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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VCD: View-Constraint Disentanglement for Action Recognition

Cited by 4 publications

References 19 publications

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

Reference-Guided Texture and Structure Inference for Image Inpainting

Background-Weakening Consistency Regularization for Semi-Supervised Video Action Detection

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