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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tarun Sharma Danelle E. Cline Duane Edgington California Institute of Technology USA MBARI USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses strategies for object detection in marine images from a practitioner’s perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： Location awareness computer vision Accuracy conferences Distributed databases Object detection Robustness

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What Makes Multimodal In-Context Learning Work?

What Makes Multimodal In-Context Learning Work?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Folco Bertini Baldassini Mustafa Shukor Matthieu Cord Laure Soulier Benjamin Piwowarski CNRS ISIR Sorbonne Université Paris France Valeo.ai Paris France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We consider the best open-source multimodal models (e.g., IDEFICS, OpenFlamingo) and a wide range of multimodal tasks. Our study unveils several noteworthy findings: (1) M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. (2) When used with advanced-ICL strategy (like RICES), M-ICL is not better than a simple strategy based on majority voting over context examples. Moreover, we identify several biases and limitations of M-ICL that warrant consideration prior to deployment. Code available at ***/folbaeni/multimodal-icl

关键词： Training Analytical models computer vision Codes Large language models Impedance matching conferences

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Calibration of Continual Learning Models

Calibration of Continual Learning Models

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lanpei Li Elia Piccoli Andrea Cossu Davide Bacciu Vincenzo Lomonaco ISTI-CNR University of Pisa University of Pisa

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction.

关键词： Continuing education computer vision conferences Computational modeling Machine learning Predictive models Data models

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Residual-based Language Models are Free Boosters for Biomedical Imaging Tasks

Residual-based Language Models are Free Boosters for Biomedi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhixin Lai Jing Wu Suiyao Chen Yucheng Zhou Naira Hovakimyan Cornel University University of Illinois at Urbana-Champaign University of South Florida University of Macau

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative encoder layer for the direct processing of visual tokens. This strategy represents a significant departure from the standard multi-modal vision-language frameworks, which typically hinge on language-driven prompts and inputs. We found that these LLMs could boost performance across a spectrum of biomedical imaging applications, including both 2D and 3D visual classification tasks, serving as plug-and-play boosters. More interestingly, as a byproduct, we found that the proposed framework achieved superior performance, setting new state-of-the-art results on extensive, standardized datasets in MedMNIST-2D and 3D. Through this work, we aim to open new avenues for employing LLMs in biomedical imaging and enriching the understanding of their potential in this specialized domain. The code is available at https://***/ZhixinLai/LLMBoostMedical

关键词： Visualization Three-dimensional displays Large language models conferences Fasteners Transformers pattern recognition

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Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images

Depth-Regularized Optimization for 3D Gaussian Splatting in ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jaeyoung Chung Jeongtaek Oh Kyoung Mu Lee Department of ECE ASRI IPAI Seoul National University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a few images are available. To address this issue, we employ an adjusted depth map as a geometric reference, derived from a pre-trained monocular depth estimation model and subsequently aligned with the sparse structure-from-motion points. We regularize the optimization process of 3D Gaussian splatting with the adjusted depth and an additional unsupervised smooth constraint, thereby effectively reducing the occurrence of floating artifacts. Our method is mainly validated on the NeRF-LLFF dataset with varying numbers of images, and we conduct multiple experiments with randomly selected training images, presenting the average value to ensure fairness. Our approach demonstrates robust geometry compared to the original method, which relied solely on images.

关键词： Training Geometry Visualization Solid modeling computer vision Three-dimensional displays conferences

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sami Barchid José Mennesson Chaabane Djéraba Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL IMT Nord Europe Institut Mines-Télécom Univ. Lille Centre for Digital Systems Lille France

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach.

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Weakly Supervised End2End Deep Visual Odometry

Weakly Supervised End2End Deep Visual Odometry

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Amin Abouee Ashwanth Ravi Lars Hinneburg Mateusz Dziwulski Florian Ölsner Jürgen Hess Stefan Milz Patrick Mäder Spleenlab GmbH Germany Technical University Ilmenau Ilmenau Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual odometry is an ill-posed problem and utilized in many robotics applications, especially automated driving for mapless navigation. Recent applications have shown that deep models outperform traditional approaches especially in localization accuracy and furthermore significantly reduce catastrophic failures. The disadvantage of most of these models is a strong dependence on high-quantity and high-quality ground truth data. However, accurate and dense depth ground truth data for real world datasets is difficult to obtain. As a result, deep models are often trained on synthetic data which introduces a domain gap. We present a weakly supervised approach to overcome this limitation. Our approach uses estimated optical flow for training that can be generated without the need for high-quality dense depth ground truth. Instead, it only requires ground truth poses and raw camera images for training. In the experiments, we show that our approach enables deep visual odometry to be efficiently trained on the target domain (real data) while achieving state-of-the-art performance on the KITTI dataset.

关键词： Training Accuracy Navigation Robot vision systems Cameras Data models pattern recognition

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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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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

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

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.

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Drone-HAT: Hybrid Attention Transformer for Complex Action recognition in Drone Surveillance Videos

Drone-HAT: Hybrid Attention Transformer for Complex Action R...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mustaqeem Khan Jamil Ahmad Abdulmotaleb El Saddik Wail Gueaieb Giulia De Masi Fakhri Karray MBZUAI Abu Dhabi UAE University of Ottawa Canada Technology Innovation Institute UAE University of Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing surveillance capabilities in sparsely populated areas. However, analyzing human activities automatically, such as "who is doing what?" in these videos, is desirable to realize their surveillance potential. In contrast, atomic visual action detection has successfully recognized such activities in movie data. However, adapting it to ultra-high resolution aerial videos is challenging because the target persons appear relatively tiny from overhead views and are sparsely located. Additionally, existing atomic visual action detection methods are based on single-label actions. However, people can perform multiple actions simultaneously, so a multi-label approach would be more appropriate. To address these problems, we propose a multi-label action detection/recognition framework using a hybrid attention vision transformer (HAT) to recognize recurrent actions more efficiently. Additionally, a multi-scale, multi-granularity module inside the action recognition transformer block extracts relevant features without redundancy. Using the Okutama Dataset, we demonstrated that our method performs better than existing state-of-the-art methodologies for interpreting aerial videos for human activity.

关键词： Training Visualization computer vision Surveillance Termination of employment Transformers Feature extraction

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luca Ostrogovich Roberto Del Prete Giuseppe Tomasicchio Nicolas Longépé Alfredo Renga Department of Industrial Engineering University of Naples Federico II Telespazio SRL Φ-Lab ESA ESRIN

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems—akin to those on-board spacecraft—were conducted. These tests are pivotal in demonstrating the system’s operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Space vehicles Accuracy Navigation Space missions Moon Software algorithms Switches

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