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Exploring the Benefits of vision Foundation Models for Unsupervised Domain Adaptation

Exploring the Benefits of Vision Foundation Models for Unsup...

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

作者： Englert, Bruno B. Piva, Fabrizio J. Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safety-critical applications, where deep-neural-network-based systems must perform reliably under various environmental conditions not seen during training. Our study investigates whether the generalization capabilities of vision Foundation Models (VFMs) and Unsupervised Domain Adaptation (UDA) methods for the semantic segmentation task are complementary. Results show that combining VFMs with UDA has two main benefits: (a) it allows for better UDA performance while maintaining the out-of-distribution performance of VFMs, and (b) it makes certain time-consuming UDA components redundant, thus enabling significant inference speedups. Specifically, with equivalent model sizes, the resulting VFM-UDA method achieves an 8.4x speed increase over the prior non-VFM state of the art, while also improving performance by +1.2 mIoU in the UDA setting and by +6.1 mIoU in terms of out-of-distribution generalization. Moreover, when we use a VFM with 3.6x more parameters, the VFM-UDA approach maintains a 3.3x speed up, while improving the UDA performance by +3.1 mIoU and the out-of-distribution performance by +10.3 mIoU. These results underscore the significant benefits of combining VFMs with UDA, setting new standards and baselines for Unsupervised Domain Adaptation in semantic segmentation. The implementation is available at https://***/tue-mps/vfmuda.

关键词： foundation model generalization semantic segmentation unsupervised domain adaptation vision foundation model

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Event-based spacecraft landing using time-to-contact

Event-based spacecraft landing using time-to-contact

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

作者： Sikorski, Olaf Izzo, Dario Meoni, Gabriele European Space Technol Ctr Adv Concepts Team Keplerlaan 1 NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flow vectors (and therefore the time to contact) and use it in a simple control feedback scheme during simulated descents. The results obtained are very encouraging, albeit insufficient to meet the stringent safety constraints and modelling accuracy imposed upon space missions. We thus conclude by discussing future work aimed at addressing these limitations.

关键词： Space vehicles computer vision Space missions conferences Computational modeling Sensors Safety

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MDMMT: Multidomain Multimodal Transformer for Video Retrieval

MDMMT: Multidomain Multimodal Transformer for Video Retrieva...

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

作者： Dzabraev, Maksim Kalashnikov, Maksim Komkov, Stepan Petiushko, Aleksandr Lomonosov Moscow State Univ Moscow Russia Huawei Moscow Res Ctr Moscow Russia

ISBN: (纸本)9781665448994

We present a new state-of-the-art on the text-to-video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved using a single model and without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that our practical approach for training on different datasets can improve test results of each other. Additionally, we check intersection between many popular datasets and show that MSRVTT as well as ActivityNet contains a significant overlap between the test and the training parts. More details are available at https://***/papermsucode/mdmmt.

关键词： Training computer vision conferences Benchmark testing pattern recognition Task analysis

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Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

Training Rare Object Detection in Satellite Imagery with Syn...

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

作者： Martinson, Eric Furlong, Bridget Gillies, Andy Soar Technol 3600 Green StSte 600 Ann Arbor MI 48105 USA

ISBN: (纸本)9781665448994

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.

关键词： Training Solid modeling computer vision Analytical models Three-dimensional displays Satellites Generative adversarial networks

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Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic Segmentation

Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic S...

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

作者： Szabo, Attila Jamali-Rad, Hadi Mannava, Siva-Datta Shell Global Solut Int BV Amsterdam Netherlands Delft Univ Technol TU Delft Delft Netherlands

ISBN: (纸本)9781665448994

Traditional empirical risk minimization (ERM) for semantic segmentation can disproportionately advantage or disadvantage certain target classes in favor of an (unfair but) improved overall performance. Inspired by the recently introduced tilted ERM (TERM), we propose tilted cross-entropy (TCE) loss and adapt it to the semantic segmentation setting to minimize performance disparity among target classes and promote fairness. Through quantitative and qualitative performance analyses, we demonstrate that the proposed Stochastic TCE for semantic segmentation can offer improved overall fairness by efficiently minimizing the performance disparity among the target classes of Cityscapes.

关键词： computer vision conferences Semantics Performance analysis pattern recognition Risk management

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Scaling Graph Convolutions for Mobile vision

Scaling Graph Convolutions for Mobile Vision

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

作者： Avery, William Munir, Mustafa Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

To compete with existing mobile architectures, MobileViG introduces Sparse vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, Mobile-ViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 AP(box) and 0.7 AP(mask), and MobileViGv2-B outperforms MobileViG-B by 1.0 AP(box) and 0.7 APmask. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU (1).

关键词： computer vision Deep Learning Edge AI Graph Neural Networks

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Video Based Computational Coding of Movement Anomalies in ASD Children

Video Based Computational Coding of Movement Anomalies in AS...

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

作者： Singh, Priya Pathak, Abhishek Ganai, Umer Jon Bhushan, Braj Subramanian, Venkatesh K. TCS Res Mumbai Maharashtra India Indian Inst Technol Kanpur Kanpur India Airbase Labs India Pvt Ltd Bengaluru India

ISBN: (纸本)9798350365474

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Early detection and diagnosis are instrumental in early intervention, yet diagnosis often remains delayed due to the limited availability of clinical practitioners and specialists. We propose a computer vision and Machine Learning based novel framework for quantitative screening of autism spectrum disorder (ASD). This is aimed to minimize the need for trained professionals at the initial stage but not substitute for it. We designed simple activities in consultation with ASD clinical psychologists and therapists for children in the 3-7 years age group that could be performed in their natural environment (home). The temporal features extracted from these activities encode the behavioral differences between Autism Spectrum Disorder (ASD) and Typically Developing (TD) control groups. Due to the unavailability of a public dataset of children performing the designed task, we created our own video dataset of 210 videos taken in unconstrained natural settings. The dataset was collected from a single RGB camera. The proposed vision and learning-based algorithms extract features from the collected data for a comprehensive set of indicators including the visual attention span, name-calling response, neck pose of the subjects, gross motor movement and establish a parametrized automated protocol for early detection without the need to take the subjects out of their natural daily environment. This forestalls the possibility of misperformance by the subject out of nervousness due to unfamiliar surroundings. Results show that our ASD screening methodology can achieve superior performance compared to the single phenotype approaches, and thus has a prognostic value that could be helpful for both clinical and research applications.

关键词： Autism Spectrum Disorder computer vision Early detection of ASD Machine Learning

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InVERGe: Intelligent Visual Encoder for Bridging Modalities in Report Generation

InVERGe: Intelligent Visual Encoder for Bridging Modalities ...

引用

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

作者： Deria, Ankan Kumar, Komal Chakraborty, Snehashis Mahapatra, Dwarikanath Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350365474

Medical image captioning plays an important role in modern healthcare, improving clinical report generation and aiding radiologists in detecting abnormalities and reducing misdiagnosis. The complex visual and textual data biases make this task more challenging. Recent advancements in transformer-based models have significantly improved the generation of radiology reports from medical images. However, these models require substantial computational resources for training and have been observed to produce unnatural language outputs when trained solely on raw image-text pairs. Our aim is to generate more detailed reports specific to images and to explain the reasoning behind the generated text through image-text alignment. Given the high computational demands of end-to-end model training, we introduce a two-step training methodology with an Intelligent Visual Encoder for Bridging Modalities in Report Generation (InVERGe) model. This model incorporates a lightweight transformer known as the Cross-Modal Query Fusion Layer (CMQFL), which utilizes the output from a frozen encoder to identify the most relevant text-grounded image embedding. This layer bridges the gap between the encoder and decoder, significantly reducing the workload on the decoder and enhancing the alignment between vision and language. Our experimental results, conducted using the MIMIC-CXR, Indiana University chest X-ray images, and CDD-CESM breast images datasets, demonstrate the effectiveness of our approach. Code: https://***/labsroy007/InVERGe

关键词： computer vision Deep Learning Large Language Model Medical Imaging Report Generation

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SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution

SMM-Conv: Scalar Matrix Multiplication with Zero Packing for...

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

作者： Ofir, Amir Ben-Artzi, Gil Ariel Univ Ariel Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buffer and can experience inefficient memory access, and (b) while GEMM is highly optimized for scientific matrices multiplications, it is not well suited for convolutions. We propose an approach that takes advantage of scalar-matrix multiplication and reduces memory overhead. Our experiments with commonly used network architectures demonstrate a significant speedup compared to existing indirect methods.

关键词： Deep learning computer vision conferences Memory management Network architecture pattern recognition Kernel

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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

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

作者： Sarkar, Sreetama Kundu, Souvik Zheng, Kai Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA Intel Labs San Diego CA USA

ISBN: (纸本)9798350365474

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4x and compute cost by up to 2x while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： on-device training token pruning vision transformer

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