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Exploiting Temporal Relations on Radar Perception for Autonomous Driving

Exploiting Temporal Relations on Radar Perception for Autono...

引用

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

作者： Li, Peizhao Wang, Pu Berntorp, Karl Liu, Hongfu Brandeis Univ Waltham MA 02453 USA Mitsubishi Elect Res Labs Cambridge MA USA MERL Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. lb enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches.

关键词： Radar Object detection Radar imaging Sensors pattern recognition Object recognition Object tracking

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It's Time for Artistic Correspondence in Music and Video

It's Time for Artistic Correspondence in Music and Video

引用

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

作者： Suris, Didac Vondrick, Carl Russell, Bryan Salamon, Justin Columbia Univ New York NY 10027 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capture the high-level concepts that are required to solve the task, we propose modeling the long-term temporal context of both the video and the music signals, using Transformer networks for each modality. Experiments show that this approach strongly outperforms alternatives that do not exploit the temporal context. The combination of our contributions improve retrieval accuracy up to 10x over prior state of the art. This strong improvement allows us to introduce a wide range of analyses and applications. For instance, we can condition music retrieval based on visually defined attributes.

关键词： computer vision Annotations Transformers pattern recognition Multiple signal classification Task analysis Context modeling

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Empowering Unsupervised Domain Adaptation with Large-scale Pre-trained vision-Language Models

Empowering Unsupervised Domain Adaptation with Large-scale P...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Li, Zhengfeng Bai, Haoping Zhang, Haotian Du, Xianzhi Shan, Jiulong Yang, Yinfei Chuah, Chen-Nee Cao, Meng Univ Calif Davis Davis CA 95616 USA Apple AI ML Cupertino CA USA Apple Cupertino CA USA

ISBN: (纸本)9798350318920;9798350318937

Unsupervised Domain Adaptation (UDA) aims to leverage the labeled source domain to solve the tasks on the unlabeled target domain. Traditional UDA methods face the challenge of the tradeoff between domain alignment and semantic class discriminability, especially when a large domain gap exists between the source and target domains. The efforts of applying large-scale pre-training to bridge the domain gaps remain limited. In this work, we propose that vision-Language Models (VLMs) can empower UDA tasks due to their training pattern with language alignment and their large-scale pre-trained datasets. For example, CLIP and GLIP have shown promising zero-shot generalization in classification and detection tasks. However, directly fine-tuning these VLMs into downstream tasks may be computationally expensive and not scalable if we have multiple domains that need to be adapted. Therefore, in this work, we first study an efficient adaption of VLMs to preserve the original knowledge while maximizing its flexibility for learning new knowledge. Then, we design a domain-aware pseudo-labeling scheme tailored to VLMs for domain disentanglement. We show the superiority of the proposed methods in four UDA-classification and two UDA-detection benchmarks, with a significant improvement (+9.9%) on DomainNet.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures vision + language and/or other modalities

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Joerg K. H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. German Canc Res Ctr DKFZ Heidelberg Div Med Image Comp Heidelberg Germany Helmholtz Informat & Data Sci Sch Hlth Karlsruhe Germany Helmholtz Informat & Data Sci Sch Hlth Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany DKFZ Natl Ctr Tumor Dis NCT NCT Heidelberg Heidelberg Germany DKFZ Interact Machine Learning Grp Heidelberg Germany DKFZ Appl Comp Vision Lab Heidelberg Germany Univ Freiburg Machine Learning Lab Freiburg Germany Latent Labs latentlabs com London England Univ Heidelberg Hosp Pattern Anal & Learning Grp Heidelberg Germany

ISBN: (纸本)9798350318920;9798350318937

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Image recognition and understanding Machine learning architectures

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Self-supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics

Self-supervised Deep Image Restoration via Adaptive Stochast...

引用

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

作者： Wang, Weixi Li, Ji Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or unsupervised methods to address the challenges and costs of collecting truth images. Based on the neuralization of a Bayesian estimator of the problem, this paper presents a self-supervised deep learning approach to general image restoration problems. The key ingredient of the neuralized estimator is an adaptive stochastic gradient Langevin dynamics algorithm for efficiently sampling the posterior distribution of network weights. The proposed method is applied on two image restoration problems: compressed sensing and phase retrieval. The experiments on these applications showed that the proposed method not only outperformed existing non-learning and unsupervised solutions in terms of image restoration quality, but also is more computationally efficient.

关键词： Deep learning computer vision Costs Image restoration Computational efficiency pattern recognition Bayes methods

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MS-EVS: Multispectral event-based vision for deep learning based face detection

MS-EVS: Multispectral event-based vision for deep learning b...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Himmi, Saad Parret, Vincent Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Comp Vision Lab Zurich Switzerland Sony Europe BV Stuttgart Lab 1 Sony Semicond Solut Europe Weybridge Surrey England

ISBN: (纸本)9798350318920;9798350318937

Event-based sensing is a relatively new imaging modality that enables low latency, low power, high temporal resolution and high dynamic range acquisition. These properties make it a highly desirable sensor for edge applications and in high dynamic range environments. As of today, most event-based sensors are monochromatic (grayscale), capturing light from a wide spectral range over the visible, in a single channel. In this paper, we introduce multispectral events and study their advantages. In particular, we consider multiple bands in the visible and near-infrared range, and explore their potential compared to monochromatic events and conventional multispectral imaging for the face detection task. We further release the first large scale bimodal face detection datasets, with RGB videos and their simulated color events, N-MobiFace and N-YoutubeFaces, and a smaller dataset with multispectral videos and events, N-SpectralFace. We find that early fusion of multispectral events significantly improves the face detection performance, compared to the early fusion of conventional multispectral images. This result shows that multispectral events carry relatively more useful information about the scene than conventional multispectral images do, with respect to their grayscale equivalent. To the best of our knowledge, our proposed method is the first exploratory research on multispectral events, specifically including near infrared data.

关键词： Algorithms Algorithms Applications Datasets and evaluations Embedded sensing / real-time techniques Image recognition and understanding

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Domain Generalization with Correlated Style Uncertainty

Domain Generalization with Correlated Style Uncertainty

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Zheyuan Wang, Bin Jha, Debesh Demir, Ugur Bagci, Ulas Northwestern Univ Machine & Hybrid Intelligence Lab Evanston IL 60208 USA

ISBN: (纸本)9798350318920;9798350318937

Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://***/freshman97/CSU.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval

FOSSIL: Free Open-Vocabulary Semantic Segmentation through S...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Barsellotti, Luca Amoroso, Roberto Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy IIT CNR Pisa Italy

ISBN: (纸本)9798350318920;9798350318937

Unsupervised Open-Vocabulary Semantic Segmentation aims to segment an image into regions referring to an arbitrary set of concepts described by text, without relying on dense annotations that are available only for a subset of the categories. Previous works rely on inducing pixel-level alignment in a multi-modal space through contrastive training over vast corpora of image-caption pairs. However, representing a semantic category solely through its textual embedding is insufficient to encompass the wide-ranging variability in the visual appearances of the images associated with that category. In this paper, we propose FOSSIL, a pipeline that enables a self-supervised backbone to perform open-vocabulary segmentation relying only on the visual modality. In particular, we decouple the task into two components: (1) we leverage text-conditioned diffusion models to generate a large collection of visual embeddings, starting from a set of captions. These can be retrieved at inference time to obtain a support set of references for the set of textual concepts. Further, (2) we exploit self-supervised dense features to partition the image into semantically coherent regions. We demonstrate that our approach provides strong performance on different semantic segmentation datasets, without requiring any additional training.

关键词： Algorithms Image recognition and understanding

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Deformable Sprites for Unsupervised Video Decomposition

Deformable Sprites for Unsupervised Video Decomposition

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

作者： Ye, Vickie Li, Zhengqi Tucker, Richard Kanazawa, Angjoo Snavely, Noah Univ Calif Berkeley Berkeley CA 94720 USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos.

关键词： Deformable models Training computer vision pattern recognition Internet Sprites (computer) Standards

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Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation

Eigencontours: Novel Contour Descriptors Based on Low-Rank A...

引用

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

作者： Park, Wonhui Jin, Dongkwon Kim, Chang-Su Korea Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the Al eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more e f ficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningfid performances on instance segmentation datasets.

关键词： Training computer vision Approximation algorithms pattern recognition Matrix decomposition

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