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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Huma Jamil Yajing Liu Turgay Caglar Christina Cole Nathaniel Blanchard Christopher Peterson Michael Kirby Computer Science Department Colorado State University Fort Collins CO USA Mathematics Department Colorado State University Fort Collins CO USA

Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and interpreting the behavior of neural networks. We utilize Representational Dissimilarity Matrices (RDMs) to investigate the coherence of data within the embedding spaces of a deep neural network. From each layer of a network, we extract and utilize bit vectors to construct similarity scores between images. From these similarity scores, we build a similarity matrix for a collection of images drawn from 2 classes. We then apply Fiedler partitioning to the associated Laplacian matrix to separate the classes. Our results indicate, through bit vector representations, that the network continues to refine class detectability with the last ReLU layer achieving better than 95% separation accuracy. Additionally, we demonstrate that bit vectors aid in adversarial image detection, again achieving over 95% accuracy in separating adversarial and non-adversarial images using a simple classifier.

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AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Calvin Yeung Kenjiro Ide Keisuke Fujii Nagoya University Japan RIKEN/JST PRESTO Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image understanding is a foundational task in computer vision, with recent applications emerging in soccer posture analysis. However, existing publicly available datasets lack comprehensive information, notably in the form of posture sequences and 2D pose annotations. Moreover, current analysis models often rely on interpretable linear models (e.g., PCA and regression), limiting their capacity to capture non-linear spatiotemporal relationships in complex and diverse scenarios. To address these gaps, we introduce the 3D Shot Posture (3DSP) dataset in soccer broadcast videos, which represents the most extensive sports image dataset with 2D pose annotations to our knowledge. Additionally, we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model, a non-linear approach for embedding pose sequences. Furthermore, we propose AutoSoccerPose, a pipeline aimed at semi-automating 2D and 3D pose estimation and posture analysis. While achieving full automation proved challenging, we provide a foundational baseline, extending its utility beyond the scope of annotated data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets, and present posture analysis results based on 3DSP. The dataset, code, and models are available at: https://***/calvinyeungck/3D-Shot-Posture-Dataset.

关键词： computer vision Analytical models Three-dimensional displays Annotations Computational modeling Pose estimation Spatiotemporal phenomena

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in2IN: Leveraging individual Information to Generate Human INteractions

in2IN: Leveraging individual Information to Generate Human I...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pablo Ruiz-Ponce German Barquero Cristina Palmero Sergio Escalera José García-Rodríguez Universidad de Alicante San Vicente del Raspeig Spain Universitat de Barcelona Barcelona Spain Computer Vision Center Cerdanyola del Vallès Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great diffi-culty in modeling the highly dimensional inter-personal dynamics. In addition, properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model, we use a large language model to extend the InterHuman dataset with individual descriptions. As a result, in2IN achieves state-of-the- art performance in the InterHuman dataset. Furthermore, in order to increase the intra-personal diversity on the existing interaction datasets, we propose DualMDM, a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result, DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.

关键词： Legged locomotion computer vision Metaverse Large language models conferences Dynamics Diversity reception

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Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Long Hoang Pham Duong Nguyen-Ngoc Tran Huy-Hung Nguyen Hyung-Joon Jeon Tai Huu-Phuong Tran Hyung-Min Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University South Korea

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest; (2) tracking objects in consecutive frames; and (3) counting objects using a track management pipeline. Several data generation techniques—including copy-and-paste, random placement, and augmentation—are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792.

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Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding

Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jay Bhanushali Manivannan Muniyandi Praneeth Chakravarthula Indian Institute of Technology Madras UNC Chapel Hill

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a cross-domain inference technique that learns from synthetic data to estimate depth and normals for in-the-wild omnidirectional 3D scenes encountered in real-world uncontrolled settings. To this end, we introduce UBotNet, an architecture that combines UNet and Bottleneck Transformer elements to predict consistent scene normals and depth. We also introduce the OmniHorizon synthetic dataset containing 24,335 omnidirectional images that represent a wide variety of outdoor environments, including buildings, streets, and diverse vegetation. This dataset is generated from expansive, lifelike virtual spaces and encompasses dynamic scene elements, such as changing lighting conditions, different times of day, pedestrians, and vehicles. Our experiments show that UBotNet achieves significantly improved accuracy in depth estimation and normal estimation compared to existing models. Lastly, we validate cross-domain synthetic-to-real depth and normal estimation on real outdoor images using UBot-Net trained solely on our synthetic OmniHorizon dataset, demonstrating the potential of both the synthetic dataset and the proposed network for real-world scene understanding applications. The dataset and accompanying code are available at ***.

关键词： Space vehicles Three-dimensional displays Pedestrians Accuracy Estimation Vegetation mapping computer architecture

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SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution

SwinFuSR: an image fusion-inspired model for RGB-guided ther...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cyprien Arnold Philippe Jouvet Lama Seoud Polytechnique Montréal Montréal Canada CHU Sainte Justine Montréal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://***/visionICLab/SwinFuSR.

关键词： Training Measurement PSNR conferences Superresolution computer architecture Transformers

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Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Asynchronous Events-based Panoptic Segmentation using Graph ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sanket Kachole Yusra Alkendi Fariborz Baghaei Naeini Dimitrios Makris Yahya Zweiri Department of Computer Science Kingston University London UK Advanced Research and Innovation Center (ARIC) Khalifa University Abu Dhabi UAE Department of Aerospace Engineering Khalifa University of Science and Technology Abu Dhabi UAE Ipsotek an Eviden Company London

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://***/sanket0707/***

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SEM-POS: Grammatically and Semantically Correct Video Captioning

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

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Asmar Nadeem Adrian Hilton Robert Dawes Graham Thomas Annin Mustafa Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey United Kingdom BBC Research and Development United Kingdom

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.

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TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks

TAB: Text-Align Anomaly Backbone Model for Industrial Inspec...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ho-Weng Lee Shang-Hong Lai Department of Computer Science National Tsing Hua University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like ImageNet. In this work, we propose a novel framework leveraging the visual-linguistic CLIP model to adeptly train a backbone model tailored to the manufacturing domain. Our approach concurrently considers visual and text-aligned embedding spaces for normal and abnormal conditions. The resulting pre-trained backbone markedly enhances performance in industrial downstream tasks, particularly in anomaly detection and localization. This improvement is substantiated through experiments conducted on multiple datasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained backbone weights allows previous works to achieve superior performance in few-shot scenarios with less training data. The proposed anomaly backbone provides a foundation model for more precise anomaly detection and localization. The source code and pre-trained weights are available at https://***/Howeng98/TAB.

关键词： Location awareness Training Visualization Source coding Training data Inspection Feature extraction

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