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Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

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A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks

A Bop and Beyond: A Second Order Optimizer for Binarized Neu...

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

作者： Daniel Suarez-Ramirez, Cuauhtemoc Gonzalez-Mendoza, Miguel Chang, Leonardo Ochoa-Ruiz, Gilberto Alberto Duran-Vega, Mario Tecnol Monterrey Sch Engn & Sci Dept Comp Sci Monterrey NL Mexico

ISBN: (纸本)9781665448994

The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation;thus, efforts are focused adapting full-precision techniques to work on BNNs. In the literature, only one previous effort has tackled the problem of directly training the BNNs with bit-flips by using the first raw moment estimate of the gradients and comparing it against a threshold for deciding when to flip a weight (Bop). In this paper, we take an approach parallel to Adam which also uses the second raw moment estimate to normalize the first raw moment before doing the comparison with the threshold, we call this method Bop2ndOrder. We present two versions of the proposed optimizer: a biased one and a bias-corrected one, each with its own applications. Also, we present a complete ablation study of the hyperparameters space, as well as the effect of using schedulers on each of them. For these studies, we tested the optimizer in CIFAR10 using the BinaryNet architecture. Also, we tested it in ImageNet 2012 with the XnorNet and BiRealNet architectures for accuracy. In both datasets our approach proved to converge faster, was robust to changes of the hyperparameters, and achieved better accuracy values.

关键词： Training computer vision conferences computer architecture Artificial neural networks pattern recognition Optimization

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GAN-based vision Transformer for High-Quality Thermal Image Enhancement

GAN-based Vision Transformer for High-Quality Thermal Image ...

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

作者： Mohamed Amine Marnissi Abir Fathallah Ecole Nationale d’Ingénieurs de Sfax Université de Sfax Sfax Tunisie Samovar CNRS Télécom SudParis Institut Polytechnique de Paris Evry Cedex France

Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector.

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Refining Biologically Inconsistent Segmentation Masks with Masked Autoencoders

Refining Biologically Inconsistent Segmentation Masks with M...

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

作者： Alexander Sauer Yuan Tian Joerg Bewersdorf Jens Rittscher Department of Engineering Science University of Oxford Department of Cell Biology Yale School of Medicine

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Microscopy images often feature regions of low signal-to-noise ratio (SNR) which leads to a considerable amount of ambiguity in the correct corresponding segmentation. This ambiguity can introduce inconsistencies in the segmentation mask which violate known biological constraints. In this work, we present a methodology which identifies areas of low SNR and refines the segmentation masks such that they are consistent with biological structures. Low SNR regions with uncertain segmentation are detected using model ensembling and selectively restored by a masked autoencoder (MAE) which leverages information about well-imaged surrounding areas. The prior knowledge of biologically consistent segmentation masks is directly learned from the *** validate our approach in the context of analysing intracellular structures, specifically by refining segmentation masks of mitochondria in expansion microscopy images with a global staining.

关键词： Image segmentation computer vision Mitochondria Microscopy conferences Biological system modeling Refining

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Phase Selective Convolution

Phase Selective Convolution

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

作者： Lin, Jamie Menjay Noorzad, Parham Yang, Yang Kwak, Nojun Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665448994

This paper introduces Phase Selective Convolution (PSC), an enhanced convolution for more deliberate utilization of activations in convolutional networks. Unlike conventional use of convolutions with activation functions, PSC preserves the full space of activations while supporting desirable model nonlinearity. Similar to several other network operations, e.g., the ReLU operation, at the time of their introduction, PSC may not execute as efficiently on platforms without hardware specialization support. As a first step in addressing the need for optimization, we propose a hardware acceleration scheme to enable the intended efficiency for PSC execution. Moreover, we propose a PSC deployment strategy, with which PSC is applied only to selected layers of the networks, to avoid excessive increase in the total model size. To evaluate the results, we apply PSC as a drop-in replacement for selected convolution layers in several networks without affecting their macro network architectures. In particular, PSC-enhanced ResNets achieve higher accuracies by 1.0-2.0% and 0.7-1.0% on CIFAR-100 and ImageNet, respectively, in Pareto efficiency. PSC-enhanced MobileNets (V2 and V3 Large) and MobileNetV3 (Small) achieve 0.9-1.0% and 1.8% accuracy gains, respectively, on ImageNet at little (0.2-0.7%) total model size increase.

关键词： Measurement Tensors Convolution Estimation Network architecture Search problems pattern recognition

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ShapeFormer: Transformer-based Shape Completion via Sparse Representation

ShapeFormer: Transformer-based Shape Completion via Sparse R...

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

作者： Yan, Xingguang Lin, Liqiang Mitra, Niloy J. Lischinski, Dani Cohen-Or, Daniel Huang, Hui Shenzhen Univ Shenzhen Peoples R China UCL London England Adobe Res London England Hebrew Univ Jerusalem Jerusalem Israel Tel Aviv Univ Tel Aviv Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant distribution can then be sampled to generate likely completions, each exhibiting plausible shape details while being faithful to the input. To facilitate the use of transformers for 3D, we introduce a compact 3D representation, vector quantized deep implicit function (VQDIF), that utilizes spatial sparsity to represent a close approximation of a 3D shape by a short sequence of discrete variables. Experiments demonstrate that ShapeFormer outperforms prior art for shape completion from ambiguous partial inputs in terms of both completion quality and diversity. We also show that our approach effectively handles a variety of shape types, incomplete patterns, and real-world scans.

关键词： Point cloud compression computer vision Three-dimensional displays Limiting Shape computer architecture Diversity methods

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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

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

作者： Yunsheng Ma Liangqi Yuan Amr Abdelraouf Kyungtae Han Rohit Gupta Zihao Li Ziran Wang College of Engineering Purdue University Toyota Motor North America InfoTech Labs

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M 2 DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR.

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Image Animation with Perturbed Masks

Image Animation with Perturbed Masks

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

作者： Shalev, Yoav Wolf, Lior Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without the knowledge of the object's structure. Furthermore, both, the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object's general pose and shape. To control the source of the identity of the output frame, we employ perturbations to interrupt the unwanted identity information on the driver's mask. A mask-refinement module then replaces the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multiscale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Due to the lack of fully supervised data, we train on the task of reconstructing frames from the same video the source image is taken from. Our method is shown to greatly outperform the state-of-the-art methods on multiple benchmarks. Our code and samples are available at https://***/itsyoayshalev/Image-Animation-with-Perturbed-Masks.

关键词： computer vision Codes Shape Perturbation methods Benchmark testing Generators Encoding

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Selective Replay Enhances Learning in Online Continual Analogical Reasoning

Selective Replay Enhances Learning in Online Continual Analo...

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

作者： Hayes, Tyler L. Kanan, Christopher Rochester Inst Technol Rochester NY 14623 USA Paige Rochester NY USA Cornell Tech New York NY USA

ISBN: (纸本)9781665448994

In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task(1).

关键词： Measurement Protocols Neural networks Reinforcement learning Streaming media Cognition pattern recognition

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OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade

OCMCTrack: Online Multi-Target Multi-Camera Tracking with Co...

引用

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

作者： Andreas Specker Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The implementation of multi-target multi-camera tracking systems in indoor environments, including shops and warehouses, facilitates strategic product positioning and the improvement of operational workflows. This paper presents the online multi-target multi-camera tracking framework OCMCTrack, which tracks the 3D positions of people in the world. The proposed framework introduces a novel matching cascade to re-evaluate track assignments dynamically, thus minimizing false positive associations often made by online trackers. Additionally, this work presents three effective methods to enhance the transformation of a person’s position in the image to world coordinates, thereby addressing common inaccuracies in positional reference points. The proposed methodology is able to achieve competitive performance in Track 1 of the 2024 AI City Challenge, demonstrating the effectiveness of the framework.

关键词： Technological innovation computer vision Three-dimensional displays Annotations conferences Urban areas Real-time systems

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