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Seeing Out of tHe bOx: End-to-End Pre-training for vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-La...

引用

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

作者： Huang, Zhicheng Zeng, Zhaoyang Huang, Yupan Liu, Bei Fu, Dongmei Fu, Jianlong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based visual features usually represent parts of an image, it is challenging for existing visionlanguage models to fully understand the semantics from paired natural languages. In this paper, we propose SOHO to "Seeing Out of tHe bOx" that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. In particular, SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in our proposed pre-training task Masked Visual Modeling (MVM). We conduct experiments on four well-established vision-language tasks by following standard VLPT settings. In particular, SOHO achieves absolute gains of 2.0% R@1 score on MSCOCO text retrieval 5k test split, 1.5% accuracy on NLVR2 test-P split, 6.7% accuracy on SNLI-VE test split, respectively.

关键词： Visualization Dictionaries Annotations Semantics Transforms Feature extraction Transformers

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Neuralizer: General Neuroimage Analysis without Re-Training

Neuralizer: General Neuroimage Analysis without Re-Training

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

作者： Czolbe, Steffen Dalca, Adrian V. Univ Copenhagen Copenhagen Denmark MGH Copenhagen Denmark MIT Cambridge MA USA Harvard Med Sch MGH Boston MA USA

ISBN: (纸本)9798350301298

Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. Yet, when presented with a new task or a dataset with different visual characteristics, practitioners most often need to train a new model, or fine-tune an existing one. This is a time-consuming process that poses a substantial barrier for the thousands of neuroscientists and clinical researchers who often lack the resources or machine-learning expertise to train deep learning models. In practice, this leads to a lack of adoption of deep learning, and neuroscience tools being dominated by classical frameworks. We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning. Tasks do not have to be known a priori, and generalization happens in a single forward pass during inference. The model can solve processing tasks across multiple image modalities, acquisition methods, and datasets, and generalize to tasks and modalities it has not been trained on. Our experiments on coronal slices show that when few annotated subjects are available, our multi-task network outperforms task-specific baselines without training on the task.

关键词： cell microscopy Medical and biological vision

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Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Learning Semantic-Aware Knowledge Guidance for Low-Light Ima...

引用

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

作者： Wu, Yuhui Pan, Chen Wang, Guoqing Yang, Yang Wei, Jiwei Li, Chongyi Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement

关键词： Low-level vision

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TransMix: Attend to Mix for vision Transformers

TransMix: Attend to Mix for Vision Transformers

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

作者： Chen, Jie-Neng Sun, Shuyang He, Ju Torr, Philip Yuille, Alan Bai, Song Johns Hopkins Univ Baltimore MD 21218 USA Univ Oxford Oxford England ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mixup-based augmentation has been found to be effective for generalizing models during training, especially for vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that the linearly interpolated ratio of targets should be kept the same as the ratio proposed in input interpolation. This may lead to a strange phenomenon that sometimes there is no valid object in the mixed image due to the random process in augmentation but there is still response in the label space. To bridge such gap between the input and label spaces, we propose TransMix, which mixes labels based on the attention maps of vision Transformers. The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map. TransMix is embarrassingly simple and can be implemented in just a few lines of code without introducing any extra parameters and FLOPs to ViT-based models. Experimental results show that our method can consistently improve various ViT-based models at scales on ImageNet classification. After pre-trained with TransMix on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection and instance segmentation. TransMix also exhibits to be more robust when evaluating on 4 different benchmarks.

关键词： Training Image segmentation Codes Semantics Object detection Benchmark testing Transformers

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SHViT: Single-Head vision Transformer with Memory Efficient Macro Design

SHViT: Single-Head Vision Transformer with Memory Efficient ...

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

作者： Yun, Seokju Ro, Youngmin Univ Seoul Machine Intelligence Lab Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Recently, efficient vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage structure at the macro level, while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore, our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions, and several attention heads in the latter stages are computationally redundant. To handle this, we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions, we introduce SHViT, a SingleHead vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example, on ImageNet-1k, our SHViT-S4 is 3.3x, 8.1x, and 2.4x faster than MobileViTv2 x1.0 on GPU, CPU, and iPhone12 mobile device, respectively, while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using MaskRCNN head, our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device, respectively.

关键词： CNNs efficient visual backbone vision Transformer

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Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras 31

Analytical Modeling of Vanishing Points and Curves in Catadi...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Miraldo, Pedro Eiras, Francisco Ramalingam, Srikumar Inst Super Tecn Lisbon Portugal Univ Oxford Oxford England Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9781538664209

Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries. A vanishing point is a point on the image plane where parallel lines in 3D space appear to converge, whereas a vanishing line passes through 2 or more vanishing points. While such concepts are simple and intuitive in perspective cameras, their counterparts in catadioptric cameras (obtained using mirrors and lenses) are more involved. For example, lines in the 3D space map to higher degree curves in catadioptric cameras. The projection of a set of 3D parallel lines converges on a single point in perspective images, whereas they converge to more than one point in catadioptric cameras. To the best of our knowledge, we are not aware of any systematic development of analytical models for vanishing points and vanishing curves in different types of catadioptric cameras. In this paper, we derive parametric equations for vanishing points and vanishing curves using the calibration parameters, mirror shape coefficients, and direction vectors of parallel lines in 3D space. We show compelling experimental results on vanishing point estimation and absolute pose estimation for a wide range of catadioptric cameras in both simulations and real experiments.

关键词： Cameras Three-dimensional displays Mirrors Mathematical model Analytical models Robot vision systems Estimation

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Indescribable Multi-modal Spatial Evaluator

Indescribable Multi-modal Spatial Evaluator

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

作者： Kong, Lingke Qi, X. Sharon Shen, Qijin Wang, Jiacheng Zhang, Jingyi Hu, Yanle Zhou, Qichao Manteia Tech Xiamen Peoples R China Univ Calif Los Angeles Los Angeles CA USA Fuzhou Univ Fuzhou Peoples R China Xiamen Univ Xiamen Peoples R China Mayo Clin Arizona Phoenix AZ 85054 USA

ISBN: (纸本)9798350301298

Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator. To optimize IMSE performance, we also proposed a new style enhancement method called Shuffle Remap which randomizes the image distribution into multiple segments, and then randomly disorders and remaps these segments, so that the distribution of the original image is changed. Shuffle Remap can help IMSE to predict the difference in spatial location from unseen target distributions. Our results show that IMSE outperformed the existing methods for registration using T1-T2 and CT-MRI datasets. IMSE also can be easily integrated into the traditional registration process, and can provide a convenient way to evaluate and visualize registration results. IMSE also has the potential to be used as a new paradigm for image-to-image translation. Our code is available at https://***/Kid-Liet/IMSE.

关键词： cell microscopy Medical and biological vision

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Self-Supervised Visibility Learning for Novel View Synthesis

Self-Supervised Visibility Learning for Novel View Synthesis

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

作者： Shi, Yujiao Li, Hongdong Yu, Xin Australian Natl Univ Canberra ACT Australia ACRV Canberra ACT Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the quality of estimated scene geometry. In this paper, we propose an end-to-end NVS framework to eliminate the error propagation issue. To be specific, we construct a volume under the target view and design a source-view visibility estimation (SVE) module to determine the visibility of the target-view voxels in each source view. Next, we aggregate the visibility of all source views to achieve a consensus volume. Each voxel in the consensus volume indicates a surface existence probability. Then, we present a soft ray-casting (SRC) mechanism to find the most front surface in the target view (i.e., depth). Specifically, our SRC traverses the consensus volume along viewing rays and then estimates a depth probability distribution. We then warp and aggregate source view pixels to synthesize a novel view based on the estimated source-view visibility and target-view depth. At last, our network is trained in an end-to-end self-supervised fashion, thus significantly alleviating error accumulation in view synthesis. Experimental results demonstrate that our method generates novel views in higher quality compared to the state-of-the-art.

关键词： Geometry Training Technological innovation computer vision Image synthesis Aggregates Estimation

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Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning 32

Object-aware Aggregation with Bidirectional Temporal Graph f...

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

作者： Zhang, Junchao Peng, Yuxin Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China

ISBN: (纸本)9781728132938

Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understand the global content of video, but also capture the detailed object information. Meanwhile, video representations have great impact on the quality of generated captions. Thus, it is important for video captioning to capture salient objects with their detailed temporal dynamics, and represent them using discriminative spatio-temporal representations. In this paper, we propose a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object-aware local feature aggregation on detected object regions. The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object. (2) Object-aware aggregation: Learnable VLAD (Vector of Locally Aggregated Descriptors) models are constructed on object temporal trajectories and global frame sequence, which performs object-aware aggregation to learn discriminative representations. A hierarchical attention mechanism is also developed to distinguish different contributions of multiple objects. Experiments on two widely-used datasets demonstrate our OA-BTG achieves state-of-the-art performance in terms of BLEU@4, METEOR and CIDEr metrics.

关键词： Video Analytics vision + Language

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AEGNN: Asynchronous Event-based Graph Neural Networks

AEGNN: Asynchronous Event-based Graph Neural Networks

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

作者： Schaefer, Simon Gehrig, Daniel Scaramuzza, Davide Univ Zurich Dept Informat Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and high temporal resolution of events, leading to high computational burden and latency. For this reason, recent works have adopted Graph Neural Networks (GNNs), which process events as "static" spatio-temporal graphs, which are inherently "sparse". We take this trend one step further by introducing Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel event-processing paradigm that generalizes standard GNNs to process events as "evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that restrict recomputation of network activations only to the nodes affected by each new event, thereby significantly reducing both computation and latency for eventby-event processing. AEGNNs are easily trained on synchronous inputs and can be converted to efficient, "asynchronous" networks at test time. We thoroughly validate our method on object classification and detection tasks, where we show an up to a 200-fold reduction in computational complexity (FLOPs), with similar or even better performance than state-of-the-art asynchronous methods. This reduction in computation directly translates to an 8fold reduction in computational latency when compared to standard GNNs, which opens the door to low-latency eventbased processing.

关键词： Power demand Object detection Market research Graph neural networks pattern recognition Object recognition Task analysis

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