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Progressive Semantic Segmentation

Progressive Semantic Segmentation

引用

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

作者： Chuong Huynh Anh Tuan Tran Khoa Luu Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam Univ Arkansas Fayetteville AR 72701 USA SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin. Code is available at https://***/VinAIResearch/MagNet.

关键词： Image segmentation computer vision Image resolution Codes Magnetic separation Magnetic resonance imaging Semantics

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AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

AdvSim: Generating Safety-Critical Scenarios for Self-Drivin...

引用

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

作者： Wang, Jingkang Pun, Ava Tu, James Manivasagam, Sivabalan Sadat, Abbas Casas, Sergio Ren, Mengye Urtasun, Raquel Univ Toronto Toronto ON Canada Uber ATG Pittsburgh PA 15201 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safetycritical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

关键词： Training computer vision Laser radar Robustness Safety Trajectory Planning

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Open World Compositional Zero-Shot Learning

Open World Compositional Zero-Shot Learning

引用

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

作者： Mancini, Massimiliano Naeem, Muhammad Ferjad Xian, Yongqin Akata, Zeynep Univ Tubingen Tubingen Germany Tech Univ Munich Munich Germany MPI Informat Saarbrucken Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665445092

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art. Code is available at:https://***/ExplainableML/czsl.

关键词： Training Degradation Visualization computer vision Computational modeling Knowledge based systems Benchmark testing

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SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

SceneGraphFusion: Incremental 3D Scene Graph Prediction from...

引用

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

作者： Wu, Shun-Cheng Wald, Johanna Tateno, Keisuke Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.

关键词： computer vision Three-dimensional displays Simultaneous localization and mapping Fuses Semantics Prediction methods Graph neural networks

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Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Depth Completion with Twin Surface Extrapolation at Occlusio...

引用

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

作者： Imran, Saif Liu, Xiaoming Morris, Daniel Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion boundaries. Here we propose a multi-hypothesis depth representation that explicitly models both foreground and background depths in the difficult occlusion-boundary regions. Our method can be thought of as performing twin-surface extrapolation, rather than interpolation, in these regions. Next our method fuses these extrapolated surfaces into a single depth image leveraging the image data. Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation. This enables us to train a network to simultaneously do surface extrapolation and surface fusion. We characterize our loss function and compare with other common losses. Finally, we validate our method on three different datasets;KITTI, an outdoor real-world dataset, NYU2, indoor real-world depth dataset and Virtual KITTI, a photo-realistic synthetic dataset with dense groundtruth, and demonstrate improvement over the state of the art.

关键词： Extrapolation Interpolation computer vision Fuses Estimation Channel estimation Robustness

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Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Smoothing the Disentangled Latent Style Space for Unsupervis...

引用

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

作者： Liu, Yahui Sangineto, Enver Chen, Yajing Bao, Linchao Zhang, Haoxian Sebe, Nicu Lepri, Bruno Wang, Wei De Nadai, Marco Univ Trento Trento Italy Tencent AI Lab Shenzhen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.

关键词： Training Interpolation computer vision Smoothing methods Protocols Computational modeling Semantics

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Temporal Query Networks for Fine-grained Video Understanding

Temporal Query Networks for Fine-grained Video Understanding

引用

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

作者： Zhang, Chuhan Gupta, Ankush Zisserman, Andrew Univ Oxford Oxford England DeepMind London England

ISBN: (纸本)9781665445092

Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. We cast this into a query-response mechanism, where each query addresses a particular question, and has its own response label set. We make the following four contributions: (i) We propose a new model-a Temporal Query Network-which enables the query-response functionality, and a structural understanding of fine-grained actions. It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query. (ii) We propose a new way-stochastic feature bank update-to train a network on videos of various lengths with the dense sampling required to respond to fine-grained queries. (iii) we compare the TQN to other architectures and text supervision methods, and analyze their pros and cons. Finally, (iv) we evaluate the method extensively on the FineGym and Diving48 benchmarks for fine-grained action classification and surpass the state-of-the-art using only RGB features. Project page: https://***/-vgg/research/tqn/.

关键词： Training Location awareness computer vision computer architecture pattern recognition Videos

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An Action recognition Approach with Context and Multiscale Motion Awareness 35

An Action Recognition Approach with Context and Multiscale M...

引用

35th SIBGRAPI conference on Graphics, patterns and Images (SIBGRAPI)

作者： Cardoso, Danilo Barros Campos, Luiza C. B. Nascimento, Erickson R. Univ Fed Minas Gerais UFMG Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Despite the substantial progress made by computer vision approaches in solving image classification, object detection, and pose estimation, to name a few, activity recognition remains one of the key challenges in computer vision and pattern recognition. This paper proposes a new learning framework based on multiscale spatiotemporal graph convolution layers and a transformer architecture. Even though several approaches present high accuracy in more traditional datasets like NTU, their performance significantly drops when tested in datasets with a high level of ambiguity among activities and an unbalanced number of samples for each class. We evaluated our architecture in the challenging BABEL dataset, where we achieved state of the art in terms of accuracy (65.4%) in action classification when considering both ambiguity and class unbalance. The source code and trained models are publicly available at https://***/verlab/AnActionrecognitionApproach_SIBGRAPI_2022.

关键词： Measurement computer vision Source coding Semantics Pose estimation computer architecture Object detection

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Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

Fashion IQ: A New Dataset Towards Retrieving Images by Natur...

引用

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

作者： Wu, Hui Gao, Yupeng Guo, Xiaoxiao Al-Halah, Ziad Rennie, Steven Grauman, Kristen Feris, Rogerio MIT IBM Watson AI Lab Cambridge MA 02142 USA IBM Res Armonk NY 10504 USA UT Austin Austin TX USA Pryon New York NY USA

ISBN: (纸本)9781665445092

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialogbased image retrieval. We believe that our dataset will encourage further work on developing more natural and realworld applicable conversational shopping assistants.(1)

关键词： Visualization computer vision Image retrieval Natural languages Clothing Buildings Ontologies

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A Lightweight Skeleton-Based 3D-CNN for Real-Time Fall Detection and Action recognition

A Lightweight Skeleton-Based 3D-CNN for Real-Time Fall Detec...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Nadhira Noor In Kyu Park Department of Electrical and Computer Engineering Inha University Incheon Korea

Implementing skeleton-based action recognition in real-world applications is a difficult task, because it involves multiple modules such as person detection and pose estimaton. In terms of context, skeleton-based approach has the strong advantage of robustness in understanding actual human actions. However, for most real-world videos in the standard benchmark datasets, human poses are not easy to detect, (i.e. only partially visible or occluded by other objects), and existing pose estimators mostly fail to detect the person during the falling motion. Thus, we propose a newly augmented human pose dataset to improve the accuracy of pose extraction. Furthermore, we propose a lightweight skeleton-based 3D-CNN action recognition network that shows significant improvement on accuracy and processing time over the baseline. Experimental results show that the proposed skeleton-based method shows high accuracy and efficiency in real world scenarios.

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