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OTA: Optimal Transport Assignment for Object Detection

OTA: Optimal Transport Assignment for Object Detection

引用

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

作者： Ge, Zheng Liu, Songtao Liu, Zeming Yoshie, Osamu Sun, Jian Waseda Univ Tokyo Japan Megvii Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem - a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1x scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios.

关键词： Training computer vision Costs Codes Transportation Estimation Object detection

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Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos

Co-Grounding Networks with Semantic Attention for Referring ...

引用

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

作者： Song, Sijie Lin, Xudong Liu, Jiaying Guo, Zongming Chang, Shih-Fu Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Columbia Univ DVMM Lab New York NY USA

ISBN: (纸本)9781665445092

In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and LiOTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset.

关键词： Visualization computer vision Correlation Grounding Computational modeling Semantics Benchmark testing

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Generalized Few-Shot Object Detection without Forgetting

Generalized Few-Shot Object Detection without Forgetting

引用

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

作者： Fan, Zhibo Ma, Yuchen Li, Zeming Sun, Jian Megvii Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that detecting all classes is crucial as test samples may contain any instances in realistic applications, which requires the few-shot detector to learn new concepts without forgetting. Through analysis on transfer learning based methods, some neglected but beneficial properties are utilized to design a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Extensive experiments on few-shot detection benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art methods on overall performance among all settings as it can achieve competitive results on few-shot classes and does not degrade the base class performance at all. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection.

关键词： Measurement computer vision Transfer learning Object detection Detectors Benchmark testing Reliability engineering

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Guided Integrated Gradients: an Adaptive Path Method for Removing Noise

Guided Integrated Gradients: an Adaptive Path Method for Rem...

引用

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

作者： Kapishnikov, Andrei Venugopalan, Subhashini Avci, Besim Wedin, Ben Terry, Michael Bolukbasi, Tolga Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Integrated Gradients (IG) [29] is a commonly used feature attribution method for deep neural networks. While IG has many desirable properties, the method often produces spurious/noisy pixel attributions in regions that are not related to the predicted class when applied to visual models. While this has been previously noted [27], most existing solutions [25, 17] are aimed at addressing the symptoms by explicitly reducing the noise in the resulting attributions. In this work, we show that one of the causes of the problem is the accumulation of noise along the IG path. To minimize the effect of this source of noise, we propose adapting the attribution path itself - conditioning the path not just on the image but also on the model being explained. We introduce Adaptive Path Methods (APMs) as a generalization of path methods, and Guided IG as a specific instance of an APM. Empirically, Guided IG creates saliency maps better aligned with the model's prediction and the input image that is being explained. We show through qualitative and quantitative experiments that Guided IG outperforms other, related methods in nearly every experiment.

关键词： Measurement Deep learning Adaptation models Visualization computer vision Computational modeling Predictive models

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ZeroScatter: Domain Transfer for Long Distance Imaging and vision through Scattering Media

ZeroScatter: Domain Transfer for Long Distance Imaging and V...

引用

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

作者： Shi, Zheng Tseng, Ethan Bijelic, Mario Ritter, Werner Heide, Felix Princeton Univ Princeton NJ 08544 USA Mercedes Benz AG Stuttgart Germany Algolux Montreal PQ Canada

ISBN: (纸本)9781665445092

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.

关键词： Training computer vision Rain Snow Scattering Training data Object detection

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Adversarial Generation of Continuous Images

Adversarial Generation of Continuous Images

引用

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

作者： Skorokhodov, Ivan Ignatyev, Savva Elhoseiny, Mohamed King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665445092

In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) - an MLP that predicts an RGB pixel value given its (x, y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real-world data. Our proposed INR-GAN architecture improves the performance of continuous image generators by several times, greatly reducing the gap between continuous image GANs and pixel-based ones. Apart from that, we explore several exciting properties of the INR-based decoders, like out-of-the-box superresolution, meaningful image-space interpolation, accelerated inference of low-resolution images, an ability to extrapolate outside of image boundaries, and strong geometric prior. The project page is located at https://***/inr-gan.

关键词： Learning systems Interpolation computer vision Image synthesis Superresolution Modulation Solids

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Pixel-wise Anomaly Detection in Complex Driving Scenes

Pixel-wise Anomaly Detection in Complex Driving Scenes

引用

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

作者： Di Biase, Giancarlo Blum, Hermann Siegwart, Roland Cadena, Cesar Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665445092

The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous areas or re-synthesizing the image from the semantic label map to find dissimilarities with the input image. In this work, we demonstrate that these two methodologies contain complementary information and can be combined to produce robust predictions for anomaly segmentation. We present a pixel-wise anomaly detection framework that uses uncertainty maps to improve over existing re-synthesis methods in finding dissimilarities between the input and generated images. Our approach works as a general framework around already trained segmentation networks, which ensures anomaly detection without compromising segmentation accuracy, while significantly out- performing all similar methods. Top-2 performance across a range of different anomaly datasets shows the robustness of our approach to handling different anomaly instances.

关键词： Image segmentation computer vision Uncertainty Computational modeling Semantics Measurement uncertainty Robustness

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VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Heesup Yun Sassoum Lo Christine H. Diepenbrock Brian N. Bailey J. Mason Earles University of California Davis CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lower the barrier to introducing thermal imaging in agricultural research and production. This paper presents an approach to improve the temperature accuracy and image quality of low-cost thermal imaging cameras for agricultural applications. Leveraging advancements in computer vision techniques, particularly deep learning networks, we propose a method, called VisTA-SR (Visual & Thermal Alignment and Super-Resolution Enhancement) that combines RGB and thermal images to enhance the capabilities of low-resolution thermal cameras. The research includes calibration and validation of temperature measurements, acquisition of paired image datasets, and the development of a deep learning network tailored for agricultural thermal imaging. Our study addresses the challenges of image enhancement in the agricultural domain and explores the potential of low-cost thermal cameras to replace high-resolution industrial cameras. Experimental results demonstrate the effectiveness of our approach in enhancing temperature accuracy and image sharpness, paving the way for more accessible and efficient thermal imaging solutions in agriculture.

关键词： Temperature measurement Deep learning computer vision Visualization Accuracy Plants (biology) Superresolution

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LayoutGMN: Neural Graph Matching for Structural Layout Similarity

LayoutGMN: Neural Graph Matching for Structural Layout Simil...

引用

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

作者： Patil, Akshay Gadi Li, Manyi Fisher, Matthew Savva, Manolis Zhang, Hao Simon Fraser Univ Burnaby BC Canada Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-theart method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.

关键词： Measurement Deep learning computer vision Convolution Computational modeling Layout Graph neural networks

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Amalgamating Knowledge from Heterogeneous Graph Neural Networks

Amalgamating Knowledge from Heterogeneous Graph Neural Netwo...

引用

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

作者： Jing, Yongcheng Yang, Yiding Wang, Xinchao Song, Mingli Tao, Dacheng Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA Natl Univ Singapore Singapore Singapore Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs). We strive to train a multi-talented student GNN, without accessing human annotations, that "amalgamates" knowledge from a couple of teacher GNNs with heterogeneous architectures and handling distinct tasks. The student derived in this way is expected to integrate the expertise from both teachers while maintaining a compact architecture. To this end, we propose an innovative approach to train a slimmable GNN that enables learning from teachers with varying feature dimensions. Meanwhile, to explicitly align topological semantics between the student and teachers, we introduce a topological attribution map (TAM) to highlight the structural saliency in a graph, based on which the student imitates the teachers' ways of aggregating information from neighbors. Experiments on seven datasets across various tasks, including multi-label classification and joint segmentation-classification, demonstrate that the learned student, with a lightweight architecture, achieves gratifying results on par with and sometimes even superior to those of the teachers in their specializations. Our code is publicly available at https://***/ycjing/***.

关键词： Knowledge engineering Convolutional codes computer vision Annotations Semantics computer architecture Graph neural networks

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