IEEE Winter Applications and Computer Vision Workshops (WACVW)
2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
4-8 Jan. 2022
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Table of Contents
Task Adaptive Network for Image Restoration with Combined Degradation Factors
Jingyuan Zhou;Chaktou Leong;Minyi Lin;Wantong Liao;Congduan Li
Publication Year: 2022,Page(s):1 - 8
Cited by: Papers (4)
Existing methods have achieved excellent performance on image restoration, but most of them are designed for one type of degradation. However, the weather is complex in the real world. So networks designed for single tasks are usually difficult to apply. Therefore, we propose a task-adaptive attention module to enable the network to restore images with multiple degradation factors. The task-adapti...Show More
A No-Reference model for Detecting Audio Artifacts using Pretrained Audio Neural Networks
Publication Year: 2022,Page(s):9 - 13
This work presents a No-Reference model to detect audio artifacts in video. The model, based upon a Pretrained Audio Neural Network, classifies a 1 second audio segment as either: No Defect, Audio Hum, Audio Hiss, Audio Distortion or Audio Clicks. The model achieves a balanced accuracy of 0.986 on our proprietary simulated dataset.Show More
RainGAN: Unsupervised Raindrop Removal via Decomposition and Composition
Publication Year: 2022,Page(s):14 - 23
Cited by: Papers (7)
Adherent raindrops on windshield or camera lens may distort and occlude vision, causing issues for downstream machine vision perception. Most of the existing raindrop removal methods focus on learning the mapping from a raindrop image to its clean content by the paired raindrop-clean images. However, the paired real-world data is difficult to collect in practice. This paper presents a novel framew...Show More
DILIE: Deep Internal Learning for Image Enhancement
Publication Year: 2022,Page(s):24 - 33
Cited by: Papers (1)
We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. The methods mostly fall into two categories: training with prior examples methods and training with no-prior examples methods. Recently, Deep Internal Learning solutions to image enhancement in training with no-prior examples setup are gaining attention. We perform i...Show More
Utilizing Network Features to Detect Erroneous Inputs
Publication Year: 2022,Page(s):34 - 43
Cited by: Papers (2)
Neural networks are vulnerable to a wide range of erroneous inputs such as corrupted, out-of-distribution, misclassified, and adversarial examples. Previously, separate solutions have been proposed for each of these faulty data types, however, in this work we show that a collective set of inputs with variegated data quality issues can be jointly identified with a single model. Specifically, we tra...Show More
Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation
Publication Year: 2022,Page(s):44 - 51
Cited by: Papers (9)
Deep learning motivated by convolutional neural networks has been highly successful in a range of medical imaging problems like image classification, image segmentation, image synthesis etc. However for validation and interpretability, not only do we need the predictions made by the model but also how confident it is while making those predictions. This is important in safety critical applications...Show More
SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining
Publication Year: 2022,Page(s):52 - 62
Cited by: Papers (34)
Deep learning algorithms have recently achieved promising deraining performance–s on both the natural and synthetic rainy datasets. As an essential low-level preprocessing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-level image restoration. That limits their performances at high-level tasks requir...Show More
This paper proposes a new approach to integrating image features for unsupervised depth completion. Instead of resorting to the image as input like existing works, we propose to employ the image to guide the learning process. Specifically, we regard dense depth as a reconstructed result of the sparse input, and formulate our model as an auto-encoder. To reduce structure inconsistency resulting fro...Show More
Subjective Quality Assessment of User-Generated Content Gaming Videos
Xiangxu Yu;Zhengzhong Tu;Zhenqiang Ying;Alan Bovik;Neil Birkbeck;Yilin Wang;Balu Adsumilli
Publication Year: 2022,Page(s):74 - 83
Cited by: Papers (11)
Benefited from the rapid development of the digital game industry, the growing popularity of online user-generated content (UGC) videos for games has accelerated the development of perceptual video quality assessment (VQA) models specifically for gaming videos. As a novel UGC type, gaming videos are recorded by gamers and uploaded to major streaming media platforms such as YouTube and Twitch, and ...Show More
FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance
Javier Hernandez-Ortega;Julian Fierrez;Luis F. Gomez;Aythami Morales;Jose Luis Gonzalez-de-Suso;Francisco Zamora-Martinez
Publication Year: 2022,Page(s):84 - 92
Cited by: Papers (10)
In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards. This type of tool for quality assessment can help to...Show More
Image Quality Assessment using Synthetic Images
Pavan C. Madhusudana;Neil Birkbeck;Yilin Wang;Balu Adsumilli;Alan C. Bovik
Publication Year: 2022,Page(s):93 - 102
Cited by: Papers (10)
Training deep models using contrastive learning has achieved impressive performances on various computer vision tasks. Since training is done in a self-supervised manner on unlabeled data, contrastive learning is an attractive candidate for applications for which large labeled datasets are hard/expensive to obtain. In this work we investigate the outcomes of using contrastive learning on synthetic...Show More
Improved EDVR Model for Robust and Efficient Video Super-Resolution
Publication Year: 2022,Page(s):103 - 111
Cited by: Papers (4)
Computer vision technologies are increasingly commonly used in daily life, and video super-resolution is gradually drawing more attention in the computer vision community. In this work, we propose an improved EDVR model to tackle the robustness and efficiency problems of the original EDVR model in video super-resolution. First, to handle the blurring situations and emphasize the effective features...Show More
Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals
Publication Year: 2022,Page(s):112 - 121
Cited by: Papers (5)
Activity detection is one of the attractive computer vision tasks to exploit the video streams captured by widely installed cameras. Although achieving impressive performance, conventional activity detection algorithms are usually designed under certain constraints, such as using trimmed and/or object-centered video clips as inputs. Therefore, they failed to deal with the multi-scale multi-instanc...Show More
GabriellaV2: Towards better generalization in surveillance videos for Action Detection
Ishan Dave;Zacchaeus Scheffer;Akash Kumar;Sarah Shiraz;Yogesh Singh Rawat;Mubarak Shah
Publication Year: 2022,Page(s):122 - 132
Cited by: Papers (23)
Activity detection has wide-reaching applications in video surveillance, sports, and behavior analysis. The existing literature in activity detection has mainly focused on benchmarks like AVA, AVA-Kinetics, UCF101-24, and JHMDB-21. However, these datasets fail to address all issues of real-world surveillance camera videos like untrimmed nature, tiny actor bounding boxes, multi-label nature of the ...Show More
Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds
Publication Year: 2022,Page(s):133 - 142
Cited by: Papers (51)
Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modellin...Show More
From Leaderboard To Operations: DIVA Transition Experiences
Bharadwaj Ravichandran;Roderic Collins;Keith Fieldhouse;Kellie Corona;Anthony Hoogs
Publication Year: 2022,Page(s):143 - 150
The IARPA Deep Intermodal Video Analytics (DIVA) program has sponsored the development of systems that detect and recognize activities in security video. During the period from September 2017 to March 2021, the development and evaluation of these systems was focused on optimizing accuracy, embodied in quantified metrics, against a large but relatively static corpus of video collected and annotated...Show More
TRM:Temporal Relocation Module for Video Recognition
Yijun Qian;Guoliang Kang;Lijun Yu;Wenhe Liu;Alexander G. Hauptmann
Publication Year: 2022,Page(s):151 - 160
Cited by: Papers (4)
One of the key differences between video and image understanding lies in how to model the temporal information. Due to the limit of convolution kernel size, most previous methods try to model long-term temporal information via sequentially stacked convolution layers. Such conventional manner doesn’t explicitly differentiate regions/pixels with various temporal receptive requirements and may suffer...Show More
Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a more comprehensive understanding of videos, we introduce the task of win-fail action recognitio...Show More
Actor-Centric Tubelets for Real-Time Activity Detection in Extended Videos
Publication Year: 2022,Page(s):172 - 181
Cited by: Papers (1)
We address the problem of detecting human and vehicle activities in long, untrimmed surveillance videos that capture a large field of view. Most existing activity detection approaches are designed for recognizing atomic human actions performed in the foreground. Therefore, they are not suitable for detecting activities in extended videos, which contain multiple actors performing co-occurring, comp...Show More
Sign Pose-based Transformer for Word-level Sign Language Recognition
Publication Year: 2022,Page(s):182 - 191
Cited by: Papers (69)
In this paper we present a system for word-level sign language recognition based on the Transformer model. We aim at a solution with low computational cost, since we see great potential in the usage of such recognition system on hand-held devices. We base the recognition on the estimation of the pose of the human body in the form of 2D landmark locations. We introduce a robust pose normalization s...Show More
Video action re-localization using spatio-temporal correlation
Akshaya Ramaswamy;Karthik Seemakurthy;Jayavardhana Gubbi;P Balamuralidhar
Publication Year: 2022,Page(s):192 - 201
Cited by: Papers (1)
Video re-localization plays an important role in locating the moments of interest in a long videos, and is critical for a variety of applications such as surveillance video monitoring and retrieving similar archived videos for further comparison and analysis. Current re-localization approaches compute a feature vector using a video query for each video frame, and explore various feature matching t...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Applications of Computer Vision Workshops (WACVW), 2022 IEEE/CVF Winter Conference on
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