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Image Change Captioning by Learning from an Auxiliary Task

Image Change Captioning by Learning from an Auxiliary Task

引用

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

作者： Hosseinzadeh, Mehrdad Wang, Yang Univ Manitoba Winnipeg MB Canada Huawei Technol Canada Markham ON Canada

ISBN: (纸本)9781665445092

We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.

关键词： Training computer vision Image color analysis Image retrieval Semantics Benchmark testing pattern recognition

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Bilinear Parameterization for Non-Separable Singular Value Penalties

Bilinear Parameterization for Non-Separable Singular Value P...

引用

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

作者： Ornhag, Marcus Valtonen Iglesias, Jose Pedro Olsson, Carl Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781665445092

Low rank inducing penalties have been proven to successfully uncover fundamental structures considered in computer vision and machine learning;however, such methods generally lead to non-convex optimization problems. Since the resulting objective is non-convex one often resorts to using standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), or other sub gradient methods, which exhibit slow convergence in the neighbourhood of a local minimum. We propose a method using second order methods, in particular the variable projection method (VarPro), by replacing the nonconvex penalties with a surrogate capable of converting the original objectives to differentiable equivalents. In this way we benefit from faster convergence. The bilinear framework is compatible with a large family of regularizers, and we demonstrate the benefits of our approach on real datasets for rigid and non-rigid structure from motion. The qualitative difference in reconstructions show that many popular non-convex objectives enjoy an advantage in transitioning to the proposed framework.(1)

关键词： computer vision Structure from motion Machine learning Convex functions pattern recognition Optimization Standards

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Remote Sensing Image Object Detection Method with Feature Denoising Fusion Module 7

Remote Sensing Image Object Detection Method with Feature De...

引用

7th ieee Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2024

作者： Chen, Penghui Li, Qishen Li, Qiufeng Wu, Zhongyu Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350339161

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models. © 2024 ieee.

关键词： Object detection

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Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

Bipartite Graph Network with Adaptive Message Passing for Un...

引用

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

作者： Li, Rongjie Zhang, Songyang Wan, Bo He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9781665445092

Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality.

关键词： Training Visualization computer vision Adaptive systems Message passing Neural networks Genomics

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You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

You See What I Want You to See: Exploring Targeted Black-Box...

引用

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

作者： Xiao, Yanru Wang, Cong Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781665445092

With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic back-end from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms.

关键词： computer vision Codes Image retrieval Multimedia databases pattern recognition Classification algorithms Image storage

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Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling 18

Localize, Group, and Select: Boosting Text-VQA by Scene Text...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Lu, Xiaopeng Fan, Zhen Wang, Yansen Oh, Jean Rose, Carolyn P. Carnegie Mellon Univ Language Technol Inst 5000 Forbes Ave Pittsburgh PA 15213 USA

ISBN: (纸本)9781665401913

As an important task in multimodal context understanding, Text-VQA (Visual Question Answering) aims at question answering through reading text information in images. It differentiates from the original VQA task as Text-VQA requires large amounts of scene-text relationship understanding, in addition to the cross-modal grounding capability. In this paper, we propose Localize, Group, and Select (LOGOS), a novel model which attempts to tackle this problem from multiple aspects. LOGOS leverages two grounding tasks to better localize the key information of the image, utilizes scene text clustering to group individual OCR tokens, and learns to select the best answer from different sources of OCR (Optical Character recognition) texts. Experiments show that LOGOS outperforms previous state-of-the-art methods on two Text-VQA benchmarks without using additional OCR annotation data. Ablation studies and analysis demonstrate the capability of LOGOS to bridge different modalities and better understand scene text.

关键词： Integrated optics Visualization computer vision Grounding conferences Computational modeling Knowledge discovery

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Single Image Depth Prediction with Wavelet Decomposition

Single Image Depth Prediction with Wavelet Decomposition

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

作者： Ramamonjisoa, Michael Firman, Michael Watson, Jamie Lepetit, Vincent Turmukhambetov, Daniyar Univ Gustave Eiffel CNRS Ecole Ponts IMAGINELIGM Champs Sur Marne Marne La Vallee France Niantic San Francisco CA USA

ISBN: (纸本)9781665445092

We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiplyadds in the decoder network.

关键词： Analytical models Neural networks Estimation computer architecture Wavelet analysis Decoding pattern recognition

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Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

Exploring Data-Efficient 3D Scene Understanding with Contras...

引用

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

作者： Hou, Ji Graham, Benjamin Niesner, Matthias Xie, Saining Tech Univ Munich Munich Germany Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

The rapid progress in 3D scene understanding has come with growing demand for data;however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited;even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary;and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.

关键词： computer vision Three-dimensional displays Annotations Semantics Training data Benchmark testing pattern recognition

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Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors

Fostering Generalization in Single-view 3D Reconstruction by...

引用

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

作者： Bechtold, Jan Tatarchenko, Maxim Fischer, Volker Brox, Thomas Bosch Ctr Artificial Intelligence Renningen Baden Wurttembe Germany Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665445092

Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy of priors at different levels of locality from ground truth input depth maps. We argue that exploiting local priors allows our method to efficiently use input observations, thus improving generalization in visible areas of novel shapes. At the same time, the combination of local and global priors enables meaningful hallucination of unobserved parts resulting in consistent 3D shapes. We show that the hierarchical approach generalizes much better than the global approach. It generalizes not only between different instances of a class but also across classes and to unseen arrangements of objects.

关键词： Training computer vision Three-dimensional displays Shape Training data computer architecture Network architecture

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GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

GDR-Net: Geometry-Guided Direct Regression Network for Monoc...

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

作者： Wang, Gu Manhardt, Fabian Tombari, Federico Ji, Xiangyang Tsinghua Univ BNRist Beijing Peoples R China Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-toend trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an indepth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://***/GDR-Net.

关键词： Learning systems Convolutional codes computer vision Pose estimation Pipelines Real-time systems pattern recognition

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