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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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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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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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OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervi...

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

作者： Gidaris, Spyros Bursuc, Andrei Puy, Gilles Komodakis, Nikos Cord, Matthieu Perez, Patrick Valeo Ai Paris France Univ Crete Iraklion Greece Sorbonne Univ Paris France

ISBN: (纸本)9781665445092

Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although effective visual representations should indeed exhibit such invariances, there are other important characteristics, such as encoding contextual reasoning skills, for which alternative reconstruction-based approaches might be better suited. With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image. Our strategy performs an online training of both the teacher network (whose role is to generate the BoW targets) and the student network (whose role is to learn representations), along with an online update of the visual-words vocabulary (used for the BoW targets). This idea effectively enables fully online BoW-guided unsupervised learning. Extensive experiments demonstrate the interest of our BoW-based strategy, which surpasses previous state-of-the-art methods (including contrastive-based ones) in several applications. For instance, in downstream tasks such Pascal object detection, Pascal classification and Places205 classification, our method improves over all prior unsupervised approaches, thus establishing new state-of-the-art results that are also significantly better even than those of supervised pre-training. We provide the implementation code https://***/valeoai/obow.

关键词： Training Vocabulary Visualization Protocols Perturbation methods Object detection pattern recognition

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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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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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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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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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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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RegionCLIP: Region-based Language-Image Pretraining

RegionCLIP: Region-based Language-Image Pretraining

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

作者： Zhong, Yiwu Yang, Jianwei Zhang, Pengchuan Li, Chunyuan Codella, Noel Li, Liunian Harold Zhou, Luowei Dai, Xiyang Yuan, Lu Li, Yin Gao, Jianfeng Univ Wisconsin Madison WI 53706 USA Microsoft Res Redmond WA USA Microsoft Cloud AI Redmond WA USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to unsatisfactory performance due to a major domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans. To mitigate this issue, we propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions, and then pretrains our model to align these region-text pairs in the feature space. When transferring our pretrained model to the open-vocabulary object detection task, our method outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Further, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets. Our code is available at https://***/microsoft/RegionCLIP.

关键词： Representation learning Visualization Technological innovation Image recognition Text recognition Transfer learning Object detection

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Semantic-Aware Multi-Label Adversarial Attacks

Semantic-Aware Multi-Label Adversarial Attacks

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

作者： Mahmood, Hassan El Hamifar, Ehsan Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

Despite its importance, generating attacks for multi-label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand, adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions, reducing the attack effectiveness. In this paper, we develop a framework for crafting effective and semantic-aware adversarial attacks for MLL. First, to obtain an attack that leads to semantically consistent predictions across all labels, we find a minimal superset of the target labels, referred to as consistent target set. To do so, we develop an efficient search algorithm over a knowledge graph, which encodes label dependencies. Next, we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally, by extensive experiments on three datasets and several MLL models, we show that our method generates both successful and semantically consistent attacks.(1)

关键词： Adversarial Attacks Adversarial Robustness Multi-label Attacks Multi-label recognition Semantic Attacks

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