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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

MobileDets: Searching for Object Detection Architectures for...

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

作者： Xiong, Yunyang Liu, Hanxiao Gupta, Suyog Akin, Berkin Bender, Gabriel Wang, Yongzhe Kindermans, Pieter-Jan Tan, Mingxing Singh, Vikas Chen, Bo Univ Wisconsin Madison WI 53705 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qual-comm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2x speedup. Code and models are available in the TensorFlow Object Detection API [16]: https://***/tensor flow/models/tree/master/research/object_detection.

关键词： Convolutional codes Image edge detection Neural networks Object detection computer architecture Network architecture Search problems

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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...

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

作者： Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of "labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

关键词： Training Adaptation models computer vision Training data Benchmark testing Data models Adversarial machine learning

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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Investigating Tradeoffs in Real-World Video Super-Resolution

Investigating Tradeoffs in Real-World Video Super-Resolution

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

作者： Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage in-dispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency (Fig. 1). Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length trade-off. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset are publicly available at https://***/ckkelvinchan/RealBasicVSR.

关键词： Degradation Training computer vision Codes Computational modeling Superresolution Video sequences

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DG-Font: Deformable Generative Networks for Unsupervised Font Generation

DG-Font: Deformable Generative Networks for Unsupervised Fon...

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

作者： Xie, Yangchen Chen, Xinyuan Sun, Li Lu, Yue East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (纸本)9781665445092

Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. However, existing methods for font generation are often in supervised learning. They require a large number of paired data, which is labor-intensive and expensive to collect. Besides, common image-to-image translation models often define style as the set of textures and colors, which cannot be directly applied to font generation. To address these problems, we propose novel deformable generative networks for unsupervised font generation (DG-Font). We introduce a feature deformation skip connection (FDSC) which predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level feature maps from the content encoder. The outputs of FDSC are fed into a mixer to generate the final results. Taking advantage of FDSC, the mixer outputs a high-quality character with a complete structure. To fill-ther improve the quality of generated images, we use three deformable convolution layers in the content encoder to learn style-invariant feature representations. Experiments demonstrate that our model generates characters in higher quality than state-of-art methods. The source code is available at https://***/ecnuycxie/DG-Font.

关键词： Deformable models computer vision Convolution Image color analysis Computational modeling Supervised learning Writing

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Roses are Red, Violets are Blue... But Should VQA expect Them To?

Roses are Red, Violets are Blue... But Should VQA expect The...

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

作者： Kervadec, Corentin Antipov, Grigory Baccouche, Moez Wolf, Christian Cesson Seyigne Orange France INSA Lyon LIRIS UMR CNRS 5205 Lyon France

ISBN: (纸本)9781665445092

Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to "reason", leading them to perform "educated guesses" instead. In this paper, we claim that the standard evaluation metric, which consists in measuring the overall in-domain accuracy, is misleading. Since questions and concepts are unbalanced, this tends to favor models which exploit subtle training set statistics. Alternatively, naively introducing artificial distribution shifts between train and test splits is also not completely satisfying. First, the shifts do not reflect real-world tendencies, resulting in unsuitable models;second, since the shifts are handcrafted, trained models are specifically designed for this particular setting, and do not generalize to other configurations. We propose the GQAOOD benchmark designed to overcome these concerns: we measure and compare accuracy over both rare and frequent question-answer pairs, and argue that the former is better suited to the evaluation of reasoning abilities, which we experimentally validate with models trained to more or less exploit biases. In a large-scale study involving 7 VQA models and 3 bias reduction techniques, we also experimentally demonstrate that these models fail to address questions involving infrequent concepts and provide recommendations for future directions of research.

关键词： Training Measurement Visualization computer vision Computational modeling Benchmark testing Knowledge discovery

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Scene Graph Expansion for Semantics-Guided Image Outpainting

Scene Graph Expansion for Semantics-Guided Image Outpainting

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

作者： Yang, Chiao-An Tan, Cheng-Yo Fan, Wan-Cyuan Yang, Cheng-Fu Wu, Meng-Lin Wang, Yu-Chiang Frank Natl Taiwan Univ New Taipei Taiwan Qualcomm Technol Inc San Diego CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing image outpainting works, we approach the above task by understanding and completing image semantics at the scene graph level. In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information. To better understand and process graph-based inputs, our SGT uniquely performs feature attention at both node and edge levels. While the former views edges as relationship regularization, the latter observes the co-occurrence of nodes for guiding the attention process. We demonstrate that, given a partial input image with its layout and scene graph, our SGT can be applied for scene graph expansion and its conversion to a complete layout. Following state-of-the-art layout-to-image conversions works, the task of image outpainting can be completed with sufficient and practical semantics introduced. Extensive experiments are conducted on the datasets of MS-COCO and Visual Genome, which quantitatively and qualitatively confirm the effectiveness of our proposed SGT and outpainting frameworks.

关键词： Visualization computer vision Image edge detection Computational modeling Semantics Layout Genomics

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Sketch, Ground, and Refine: Top-Down Dense Video Captioning

Sketch, Ground, and Refine: Top-Down Dense Video Captioning

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

作者： Deng, Chaorui Chen, Shizhe Chen, Da He, Yuan Wu, Qi Univ Adelaide Adelaide SA Australia INRIA Rocquencourt France Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the definitions of events are diverse which could be as simple as a single action or as complex as a set of events, depending on different semantic contexts. Therefore, directly detecting events based on video information is ill-defined and hurts the coherency and accuracy of generated dense captions. In this work, we reverse the predominant "detect-then-describe" fashion, proposing a top-down way to first generate paragraphs from a global view and then ground each event description to a video segment for detailed refinement. It is formulated as a Sketch, Ground, and Refine process (SGR). The sketch stage first generates a coarse-grained multi-sentence paragraph to describe the whole video, where each sentence is treated as an event and gets localised in the grounding stage. In the refining stage, we improve captioning quality via refinement-enhanced training and dual-path cross attention on both coarse-grained event captions and aligned event segments. The updated event caption can further adjust its segment boundaries. Our SGR model outperforms state-of-the-art methods on ActivityNet Captioning benchmark under traditional and story-oriented dense caption evaluations.

关键词： Training computer vision Codes Grounding Computational modeling Semantics Benchmark testing

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Why Object Detectors Fail: Investigating the Influence of the Dataset

Why Object Detectors Fail: Investigating the Influence of th...

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

作者： Miller, Dimity Goode, Georgia Bennie, Callum Moghadam, Peyman Jurdak, Raja Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Robot & Autonomous Syst Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A false negative in object detection describes an object that was not correctly localised and classified by a detector. In prior work, we introduced five 'false negative mechanisms' that identify the specific component inside the detector architecture that failed to detect the object. Using these mechanisms, we explore how different computer vision datasets and their inherent characteristics can influence object detector failures. Specifically, we investigate the false negative mechanisms of Faster R-CNN and RetinaNet across five computer vision datasets, namely Microsoft COCO, Pascal VOC, ExDark, ObjectNet, and COD10K. Our results show that object size and class influence the false negative mechanisms of object detectors. We also show that comparing the false negative mechanisms of a single object class across different datasets can highlight potentially unknown biases in datasets.

关键词： computer vision conferences Detectors Object detection computer architecture Market research pattern recognition

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SimPoE: Simulated Character Control for 3D Human Pose Estimation

SimPoE: Simulated Character Control for 3D Human Pose Estima...

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

作者： Yuan, Ye Wei, Shih-En Simon, Tomas Kitani, Kris Saragih, Jason Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665445092

Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation Dynamics Kinematics Reinforcement learning

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