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CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval 29

CP-mtML: Coupled Projection multi-task Metric Learning for L...

引用

2016 ieee conference on computer vision and pattern recognition (CVPR)

作者： Bhattarai, Binod Sharma, Gaurav Jurie, Frederic Univ Caen Caen France MPI Informat Saarbrucken Germany IIT Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781467388511

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-) similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.

关键词： Local binary pattern

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Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension 30

Are You Smarter Than A Sixth Grader? Textbook Question Answe...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kembhavi, Aniruddha Seo, Minjoon Schwenk, Dustin Choi, Jonghyun Farhadi, Ali Hajishirzi, Hannaneh Allen Inst Artificial Intelligence Seattle WA 98013 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781538604571

We introduce the task of Multi-Modal Machine Comprehension ((MC)-C-3), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that includes 1,076 lessons and 26,260 multi-modal questions, taken from middle school science curricula. Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets. We extend state-of-the-art methods for textual machine comprehension and visual question answering to the TQA dataset. Our experiments show that these models do not perform well on TQA. The presented dataset opens new challenges for research in question answering and reasoning across multiple modalities.

关键词： Knowledge discovery Visualization Cognition Training Natural languages computer vision

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Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

Fingerprinting Deep Neural Networks Globally via Universal A...

引用

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

作者： Peng, Zirui Li, Shaofeng Chen, Guoxing Zhang, Cheng Zhu, Haojin Xue, Minhui Shanghai Jiao Tong Univ Shanghai Peoples R China Ohio State Univ Columbus OH 43210 USA CSIRO Data61 Canberra ACT Australia Univ Adelaide Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a novel and practical mechanism to enable the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive learning that takes fingerprints as inputs, outputs a similarity score. Extensive studies show that our framework can detect model Intellectual Property (IP) breaches with confidence > 99.99 % within only 20 fingerprints of the suspect model. It also has good generalizability across different model architectures and is robust against post-modifications on stolen models.

关键词： Deep learning computer vision Computational modeling Perturbation methods Neural networks Intellectual property computer architecture

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Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

Neumann Network with Recursive Kernels for Single Image Defo...

引用

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

作者： Quan, Yuhui Wu, Zicong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9798350301298

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.

关键词： Low-level vision

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HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds 32

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for S...

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

作者： Gu, Xiuye Wang, Yijie Wu, Chongruo Lee, Yong Jae Wang, Panqu Stanford Univ Stanford CA 94305 USA TuSimple San Diego CA USA Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781728132938

We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, and fuse information from two consecutive point clouds. Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost. Our model can efficiently process a pair of point cloud frames at once with a maximum of 86K points per frame. Our approach achieves state-of-the-art performance on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Moreover, trained on synthetic data, our approach shows great generalization ability on real-world data and on different point densities without fine-tuning.

关键词： 3D from Multiview and Sensors Deep Learning Low-level vision Motion and Tracking Robotics + Driving Scene Analysis and Unders

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GROUNDHOG : Grounding Large Language Models to Holistic Segmentation

GROUNDHOG : Grounding Large Language Models to Holistic Segm...

引用

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

作者： Zhang, Yichi Qiao, Zhiqiao Gao, Xiaofeng Shakiah, Suhaila Gao, Qiaozi Chai, Joyce Univ Michigan Ann Arbor MI 48109 USA Amazon AGI Seattle WA USA

ISBN: (纸本)9798350353006

Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm lacks pixel-level representations that are important for fine-grained visual understanding and diagnosis. In this work, we introduce GROUNDHOG, an MLLM developed by grounding Large Language Models to holistic segmentation. GROUNDHOG incorporates a masked feature extractor and converts extracted features into visual entity tokens for the MLLM backbone, which then connectsgroundable phrases to unified grounding masks by retrieving and merging the entity masks. To train GROUNDHOG, we carefully curated M3G2, a grounded visual instruction tuning dataset with Multi-Modal Multi-Grained Grounding, by harvesting a collection of segmentation-grounded datasets with rich annotations. Our experimental results show that GROUNDHOG achieves superior performance on various language grounding tasks without task-specific fine-tuning, and significantly reduces object hallucination. GROUNDHOG also demonstrates better grounding towards complex forms of visual input and provides easy-to-understand diagnosis in failure cases.

关键词： Language Grounding Multi-Modal vision-Language Model

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LARGE-SCALE STEREO vision 11

LARGE-SCALE STEREO VISION

引用

conference A: computer vision and Applications, at the 11th IAPR International conference on pattern recognition

作者： TAKAHASHI, H TOMITA, F SANYO ELECT CO TSUKUBA RES CTRTSUKUBAIBARAKI 305JAPAN

ISBN: (纸本)081862910X

In the case of stereo measuring in the 3-dimensional world, it is difficult to obtain a sufficient accuracy in an outside environment. We propose to move a single camera and to prolong the base line, then measure the motion parameters of the camera by image correspondence. We use primitives as units of correspondence to make the matching stable. This paper presents our method and some experimental results. © 1992 ieee.

关键词： Stereo vision

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Patch-based Progressive 3D Point Set Upsampling 32

Patch-based Progressive 3D Point Set Upsampling

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang Yifan Shihao Wu Hui Huang Cohen-Or, Daniel Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Shenzhen Univ Shenzhen Peoples R China Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781728132938

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the- art learning-based [58, 59], and optimazation-based [23] approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details. The data and code are at http://***/yifita/3pu.

关键词： 3D from Multiview and Sensors Deep Learning vision + Graphics

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Pixel-Adaptive Convolutional Neural Networks 32

Pixel-Adaptive Convolutional Neural Networks

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Su, Hang Jampani, Varun Sun, Deqing Gallo, Orazio Learned-Miller, Erik Kautz, Jan UMass Amherst Amherst MA 01003 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781728132938

Convolutions are the fundamental building blocks of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it is also a major limitation, as it makes convolutions content-agnostic. We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are multiplied with a spatially varying kernel that depends on learnable, local pixel features. PAC is a generalization of several popular filtering techniques and thus can be used for a wide range of use cases. Specifically, we demonstrate state-of-the-art performance when PAC is used for deep joint image upsampling. PAC also offers an effective alternative to fully-connected CRF (Full-CRF), called PAC-CRF, which performs competitively compared to Full-CRF, while being considerably faster. In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.

关键词： Deep Learning Low-level vision Scene Analysis and Understanding

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Efficient Fine-grained Classification and Part Localization Using One Compact Network 16

Efficient Fine-grained Classification and Part Localization ...

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16th ieee International conference on computer vision (ICCV)

作者： Dai, Xiyang Southall, Ben Nhon Trinh Matei, Bogdan Univ Maryland College Pk MD 20742 USA SRI Int Princeton NJ USA

ISBN: (纸本)9781538610343

Fine-grained classification of objects such as vehicles, natural objects and other classes is an important problem in visual recognition. It is a challenging task because small and localized differences between similar looking objects indicate the specific fine-grained label. At the same time, accurate classification needs to discount spurious changes in appearance caused by occlusions, partial views and proximity to other clutter objects in scenes. A key contributor to fine-grained recognition are discriminative parts and regions of objects. Past work has often attempted to solve the problems of classification and part localization separately resulting in complex models and ad-hoc algorithms, leading to low performance in accuracy and processing time. We propose a novel multi-task deep network architecture that jointly optimizes both localization of parts and fine-grained class labels by learning from training data. The localization and classification sub-networks share most of the weights, yet have dedicated convolutional layers to capture finer level class specific information. We design our model as memory and computational efficient so that can be easily embedded in mobile applications. We demonstrate the effectiveness of our approach through experiments that achieve a new state-of-the-art 93.1% performance on the Stanford Cars-196 dataset, with a significantly smaller multi-task network (30M parameters) and significantly faster testing speed (78 FPS) compared to recent published results.

关键词： Training computer architecture Birds Convolution Switches Automobiles

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