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SEM-POS: Grammatically and Semantically Correct Video Captioning

SEM-POS: Grammatically and Semantically Correct Video Captio...

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

作者： Nadeem, Asmar Hilton, Adrian Dawes, Robert Thomas, Graham Mustafa, Annin United Kingdom Bbc Research and Development United Kingdom

ISBN: (纸本)9798350302493

Generating grammatically and semantically correct captions in video captioning is a challenging task.. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-local fusion network, with a Global-Local Fusion Block (GLFB) that encodes and fuses features from different parts of speech (POS) components with visual-spatial features. We use novel combinations of different POS components - 'determinant + subject', 'auxiliary verb', 'verb', and 'determinant + object' for supervision of the POS blocks - Det + Subject, Aux Verb, Verb, and Det + Object respectively. The novel global-local fusion network together with POS blocks helps align the visual features with language description to generate grammatically and semantically correct captions. Extensive qualitative and quantitative experiments on benchmark MSVD and MSRVTT datasets demonstrate that the proposed approach generates more grammatically and semantically correct captions compared to the existing methods, achieving the new state-of-the-art. Ablations on the POS blocks and the GLFB demonstrate the impact of the contributions on the proposed method. © 2023 IEEE.

关键词： computer vision

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Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Investigating Catastrophic Overfitting in Fast Adversarial T...

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

作者： He, Zhengbao Li, Tao Chen, Sizhe Huang, Xiaolin Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9798350302493

Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation"phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO. © 2023 IEEE.

关键词： computer vision

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Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding

Mushroom Segmentation and 3D Pose Estimation from Point Clou...

引用

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

作者： Retsinas, George Efthymiou, Niki Maragos, Petros National Technical University of Athens School of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350302493

Modern agricultural applications rely more and more on deep learning solutions. However, training well-performing deep networks requires a large amount of annotated data that may not be available and in the case of 3D annotation may not even be feasible for human annotators. In this work, we develop a deep learning approach to segment mushrooms and estimate their pose on 3D data, in the form of point clouds acquired by depth sensors. To circumvent the annotation problem, we create a synthetic dataset of mushroom scenes, where we are fully aware of 3D information, such as the pose of each mushroom. The proposed network has a fully convolutional backbone, that parses sparse 3D data, and predicts pose information that implicitly defines both instance segmentation and pose estimation task. We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors. © 2023 IEEE.

关键词： Convolution

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A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

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

作者： Ravishankar, Joshitha Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350302493

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme's superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec. © 2023 IEEE.

关键词： Dynamic mode decomposition

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Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery

Lost in Compression: the Impact of Lossy Image Compression o...

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

作者： Bhowmik, Neelanjan Barker, Jack W. Gaus, Yona Falinie A. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal) imagery. Our study quantitatively evaluates the affect that increasing levels of lossy compression has upon the performance of characteristically diverse object detection architectures (Cascade-RCNN, FSAF and Deformable DETR) with respect to varying sizes of objects present in the dataset. When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0.823 with Cascade R-CNN across the FLIR dataset, outperforming prior work. The impact of the lossy compression is more extreme at higher compression levels (15, 10, 5) across all three CNN architectures. However, re-training models on lossy compressed imagery notably ameliorated performances for all three CNN models with an average increment of similar to 76% (at higher compression level 5). Additionally, we demonstrate the relative sensitivity of differing object areas {tiny, small, medium, large} with respect to the compression level. We show that tiny and small objects are more sensitive to compression than medium and large objects. Overall, Cascade R-CNN attains the maximal mAP across most of the object area categories.

关键词： Training Image coding Sensitivity Transform coding Object detection computer architecture Transformers

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Exploring Motion Information for Distractor Suppression in Visual Tracking

Exploring Motion Information for Distractor Suppression in V...

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

作者： Liu, Kaiwen Gao, Jin Liu, Haowei Li, Liang Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch AI Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Beijing Inst Basic Med Sci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the past few years, Siamese networks have achieved outstanding improvements in visual object tracking. However, visual distractors with similar semantics can be easily misclassified as the target by Siamese networks and may consequently result in the drift problem. Besides, the Hanning window penalty, which is generally used to suppress distractors, could fail in many challengeable scenes. Notably, most failures violate the assumption of motion continuity. Thus, in this work, we explore motion information to mitigate the drift problem in visual tracking. First, we introduce a simple linear Kalman filter to predict the bounding box of the target in the current frame, which acts as a reference for decisions. Second, an IoU-Guided penalty is assembled in the post-processing to suppress distractors effectively. It's worth mentioning that our method is almost cost-free. We conduct numerous experimental validations and analyses of our approach on several challenging sequences and datasets. Our tracker runs at approximately 40 fps and performs well on those sequences which include the Background Clutter attribute. Finally, by simultaneously integrating the IoU-Guidedpenalty and the Hanning window penalty with a strong baseline tracker TransT [7], our method achieves favorable gains by 69.1 -> 71.5, 65.7 -> 66.7, 64.9 -> 65.9 success on OTB-100 [32], LaSOT [12], NFS[18].

关键词： Training Visualization computer vision Target tracking Statistical analysis conferences Semantics

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OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated...

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

作者： Chen, Dengsheng Tan, Vince Junkai Lu, Zhilin Wu, Enhua Hu, Jie Meituan China Bytedance Inc. China Tsinghua University China University of Macau China State Key Lab of Computer Science Iscas China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning. The source code of OpenFed is publicly available online at https://***/FederalLab/OpenFed. © 2023 IEEE.

关键词： Data transfer

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t-RAIN: Robust generalization under weather-aliasing label shift attacks

t-RAIN: Robust generalization under weather-aliasing label s...

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

作者： Marathe, Aboli Prabhu, Sanjana Carnegie Mellon University Machine Learning Department PittsburghPA15213 United States Carnegie Mellon University Department of Electrical and Computer Engineering PittsburghPA15213 United States

ISBN: (纸本)9798350302493

In the classical supervised learning settings, classifiers are fit with the assumption of balanced label distributions and produce remarkable results on the same. In the real world, however, these assumptions often bend and in turn adversely impact model performance. Identifying bad learners in skewed target distributions is even more challenging. Thus achieving model robustness under these "label shift"settings is an important task in autonomous perception. In this paper, we analyze the impact of label shift on the task of multi-weather classification for autonomous vehicles. We use this information as a prior to better assess pedestrian detection in adverse weather. We model the classification performance as an indicator of robustness under 4 label shift scenarios and study the behavior of multiple classes of models. We propose t-RAIN a similarity mapping technique for synthetic data augmentation using large scale generative models and evaluate the performance on DAWN dataset. This mapping boosts model test accuracy by 2.1, 4.4, 1.9, 2.7 % in no-shift, fog, snow, dust shifts respectively. We present state-of-the-art pedestrian detection results on real and synthetic weather domains with best performing 82.69 AP (snow) and 62.31 AP (fog) respectively. © 2023 IEEE.

关键词： Snow

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Robustness of Visual Explanations to Common Data Augmentation Methods

Robustness of Visual Explanations to Common Data Augmentatio...

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

作者： Tětková, Lenka Hansen, Lars Kai Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads 321 Lyngby2800 Kgs. Denmark

ISBN: (纸本)9798350302493

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not. © 2023 IEEE.

关键词： Deep neural networks

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Flow cytometry with event-based vision and spiking neuromorphic hardware

Flow cytometry with event-based vision and spiking neuromorp...

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

作者： Abreu, Steven Gouda, Muhammed Lugnan, Alessio Bienstman, Peter University of Groningen Groningen Netherlands Ghent University - Imec Ghent Belgium

ISBN: (纸本)9798350302493

Imaging flow cytometry systems play a critical role in the identification and characterization of large populations of cells or micro-particles. Such systems typically leverage deep artificial neural networks to classify samples. Here we show that an event-based camera and neuromorphic processor can be used in a flow cytometry setup to solve a binary particle classification task with less memory usage, and promising improvements in latency and energy scaling. To reduce the complexity of the spiking neural network, we combine the event-based camera with a free-space optical setup which acts as a non-linear high-dimensional feature map that is computed at the speed of light before the event-based camera receives the signal. We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. Our best artificial neural network on frames of the same data reaches only 97.51%, establishing a neuromorphic advantage also in classification accuracy. We further show that our system will scale favorably to more complex classification tasks. We pave the way for real-time classification with throughput of up to 1,000 samples per second and open up new possibilities for online and on-chip learning in flow cytometry applications. © 2023 IEEE.

关键词： Cameras

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