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DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos

DeepSegmenter: Temporal Action Localization for Detecting An...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Aboah, Armstrong Bagci, Ulas Mussah, Abdul Rashid Owor, Neema Jakisa Adu-Gyamfi, Yaw Northwestern University Department of Radiology United States University of Missouri-Columbia Department of Civil Engineering United States

ISBN: (纸本)9798350302493

Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. The code is available at https://***/aboah1994/***. © 2023 ieee.

关键词： computer vision

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Natural Language-Based Vehicle Retrieval with Explicit Cross-Modal Representation Learning

Natural Language-Based Vehicle Retrieval with Explicit Cross...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Bocheng Xiong, Yihua Zhang, Rui Feng, Yanyi Wu, Haifeng Terminus Technol Dept AI R&D Beijing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On the account of the explosive growth in the large-scale transportation videos, vehicle retrieval plays an important role in the public transportation security and the intelligent transport system recently. Most vehicle retrieval algorithms are vision-based and consist of vehicle re-identification and vehicle tracking. However, the performance of vision-based vehicle retrieval algorithms is constrained as the limited information provided by traffic video streams. In this paper, we propose a contrastive cross-modal vehicle retrieval solution, maximizing the value of the complementation between natural language representation and vision representation. The framework of the proposed solution includes: (1) Preprocess a source video in four ways for generating local motional semantics and global motional semantics;(2) Correspondingly, preprocess relevant description sentences in two ways, including Textual Local Instance Semantics Extraction (TLISE) and Textual Local Motional Semantics Extraction (TLMSE);(3) Use a two-stream architecture model with four visual encoders and four text encoders to extract visual features and textual embeddings;(4) Fuse visual features and textual embeddings respectively by concatenating them along the feature channel in the order of importance, and use them for retrieval. By using the proposed solution, we achieved MRR score of 33.20%, ranking the 7th place in the AI City Challenge 2022 Track 2.

关键词： Representation learning Visualization Semantics Urban areas computer architecture Feature extraction Robustness

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TempT: Temporal consistency for Test-time adaptation

TempT: Temporal consistency for Test-time adaptation

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Mutlu, Onur Cezmi Honarmand, Mohammadmahdi Surabhi, Saimourya Wall, Dennis P. Stanford University United States

ISBN: (纸本)9798350302493

We introduce Temporal consistency for Test-time adaptation (TempT), a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across sequential frames as a self-supervision signal. TempT is an approach with broad potential applications in computer vision tasks, including facial expression recognition (FER) in videos. We evaluate TempT's performance on the AffWild2 dataset. Our approach focuses solely on the unimodal visual aspect of the data and utilizes a popular 2D CNN backbone, in contrast to larger sequential or attention-based models used in other approaches. Our preliminary experimental results demonstrate that TempT has competitive performance compared to the previous years' reported performances, and its efficacy provides a compelling proof-of-concept for its use in various real-world applications. © 2023 ieee.

关键词： computer vision

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An Improved Association Pipeline for Multi-Person Tracking

An Improved Association Pipeline for Multi-Person Tracking

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Stadler, Daniel Beyerer, Jürgen Karlsruhe Institute of Technology Germany Fraunhofer IOSB Germany Fraunhofer Center for Machine Learning Germany

ISBN: (纸本)9798350302493

The association task of assigning detections to tracks in multi-person tracking has recently been improved by integration of a second matching stage for low-confident detections that are usually discarded in the tracking process. Despite its success, we find that this two stage matching has some weaknesses. For example, high-confident detections are preferred over low-confident detections in any case, even if the low-confident ones are more accurate. Therefore, a Combined Matching (CM) is proposed which considers all possible assignments simultaneously in a single matching stage and thus improves the association accuracy. Moreover, shortcomings of existing motion and appearance distance combinations are identified and a novel Combined Distance (CD) for motion and appearance information is introduced that significantly outperforms previous fusion approaches. Furthermore, we propose an Occlusion Aware Initialization (OAI) which prevents the start of ghost tracks from duplicate detections under occlusion. The effectiveness of our components is shown with extensive ablative experiments and the competitiveness of our tracker is demonstrated on the MOT17 and MOT20 benchmarks, where the current state-of-the-art is notably surpassed. © 2023 ieee.

关键词： computer vision

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MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion

MSTRIQ: No Reference Image Quality Assessment Based on Swin ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jing Fan, Haotian Hou, Xiaoxia Xu, Yitian Li, Tao Lu, Xuechao Fu, Lean ByteDance Inc Bldg 10Zone ABusiness PkLane 888Tianlin Rd Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains unsettled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications.

关键词： Image quality Training computer vision Predictive models Transformers Prediction algorithms Distortion

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Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices

Efficient Multi-Purpose Cross-Attention Based Image Alignmen...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bilecen, Bahri Batuhan Fisne, Alparslan Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665487399

Image alignment, also known as image registration, is a critical block used in many computer vision problems. One of the key factors in alignment is efficiency, as inefficient aligners can cause significant overhead to the overall problem. In the literature, there are some blocks that appear to do the alignment operation, although most do not focus on efficiency. Therefore, an image alignment block which can both work in time and/or space and can work on edge devices would be beneficial for almost all networks dealing with multiple images. Given its wide usage and importance, we propose an efficient, cross-attention-based, multi-purpose image alignment block (XABA) suitable to work within edge devices. Using cross-attention, we exploit the relationships between features extracted from images. To make cross-attention feasible for real-time image alignment problems and handle large motions, we provide a pyramidal block based cross-attention scheme. This also captures local relationships besides reducing memory requirements and number of operations. Efficient XABA models achieve real-time requirements of running above 20 FPS performance on NVIDIA Jetson Xavier with 30W power consumption compared to other powerful computers. Used as a sub-block in a larger network, XABA also improves multi-image super-resolution network performance in comparison to other alignment methods.

关键词： Performance evaluation computer vision Power demand Image edge detection Superresolution Feature extraction Optical imaging

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Transformer-based Multimodal Information Fusion for Facial Expression Analysis

Transformer-based Multimodal Information Fusion for Facial E...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Wei Qiu, Feng Wang, Suzhen Zeng, Hao Zhang, Zhimeng An, Rudong Ma, Bowen Ding, Yu Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human affective behavior analysis has received much attention in human-computer interaction (HCI). In this paper, we introduce our submission to the CVPR 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To fully exploit affective knowledge from multiple views, we utilize the multimodal features of spoken words, speech prosody, and facial expression, which are extracted from the video clips in the Aff-Wild2 dataset. Based on these features, we propose a unified transformer-based multimodal framework for Action Unit detection and also expression recognition. Specifically, the static vision feature is first encoded from the current frame image. At the same time, we clip its adjacent frames by a sliding window and extract three kinds of multimodal features from the sequence of images, audio, and text. Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features. The cross-attention module in the fusion module makes the output integrated features focus on the crucial parts that facilitate the downstream detection tasks. We also leverage some data balancing techniques, data augmentation techniques, and postprocessing methods to further improve the model performance. In the official test of ABAW3 Competition, our model ranks first in the EXPR and AU tracks. The extensive quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset, prove the effectiveness of our proposed method.

关键词： Human computer interaction Gold Face recognition conferences Feature extraction Transformers Data models

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Localized Latent Updates for Fine-Tuning vision-Language Models

Localized Latent Updates for Fine-Tuning Vision-Language Mod...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ibing, Moritz Lim, Isaak Kobbelt, Leif Rwth Aachen University Visual Computing Institute Germany

ISBN: (纸本)9798350302493

Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable that updating the model is fast and that the model does not lose its capabilities on data outside of the dataset, as is often the case with classical fine-tuning approaches. In this work we suggest a lightweight adapter that only updates the models predictions close to seen datapoints. We demonstrate the effectiveness and speed of this relatively simple approach in the context of few-shot learning, where our results both on classes seen and unseen during training are comparable with or improve on the state of the art. © 2023 ieee.

关键词： computer vision

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DeepACO: A Robust Deep Learning-based Automatic Checkout System

DeepACO: A Robust Deep Learning-based Automatic Checkout Sys...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Long Hoang Pham Duong Nguyen-Ngoc Tran Huy-Hung Nguyen Tai Huu-Phuong Tran Hyung-Joon Jeon Hyung-Min Jeon Jae Wook Jeon Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The retail industry has seen an increasing growth of artificial intelligence and computer vision applications. Of the various topics, automatic checkout (ACO) in retail stores or supermarkets has emerged as one of the critical tasks in this area. Several problems stem from real-world scenarios such as object occlusion, blurring from scanning motion, and similarity in scanned items. Moreover, the challenge also comes from the difficulty of collecting training images that reflect the realistic checkout scenarios due to continuous updates of the products. This paper proposes a deep learning-based automatic checkout system (DeepACO) to recognize, localize, track, and count products as they move along a retail check-out conveyor belt. The DeepACO follows the detect-and-track approach, i.e., applying trackers on detected bounding boxes. It also provides a completed pipeline for generating large training datasets under various environments from synthetic data. The proposed system has been evaluated on the 2022 AI City Challenge Track 4 benchmark. Compared to other state-of-the-art solutions, it has shown outstanding results, achieving top-2 on the test-set A with the F1 score of 0.4783.

关键词： Training computer vision Image resolution Tracking Pipelines Urban areas Benchmark testing

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Unmasking Your Expression: Expression-Conditioned GAN for Masked Face Inpainting

Unmasking Your Expression: Expression-Conditioned GAN for Ma...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Sola, Sridhar Gera, Darshan University of Birmingham Birmingham United Kingdom Sri Sathya Sai Institute of Higher Learning Bengaluru India

ISBN: (纸本)9798350302493

As face masks continue to be a part of our daily lives, the challenge of reconstructing occluded faces remains relevant. While several approaches have been proposed for removing masks from neutral facial images, few have explored the use of facial expressions as a dominant feature for reconstruction of expressive faces. To address this gap, we propose an expression-conditioned GAN (ECGAN) for reconstructing masked faces with a specified expression. Our approach leverages both the binary segmentation map of the mask and an expression label to generate high-quality images. To train our ECGAN in a supervised manner, we synthesize masked images using the RAFDB dataset to create non-masked-masked pairs of images for training. We evaluate of our approach on the RAFDB test set, demonstrating its effectiveness in generating realistic images that convincingly belong to the given expression class. This is further highlighted by comparing it to a baseline model and a state-of-the-art approach without expression-input. The code is available at https://***/SridharSola/ECGAN. © 2023 ieee.

关键词： computer vision

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