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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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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

来源：评论

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Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

Enhancing Quality of Compressed Images by Mitigating Enhance...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Qunliang Xing Mai Xu Shengxi Li Xin Deng Meisong Zheng Huaida Liu Ying Chen Beihang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts, thus degrading their perceptual quality. In this paper, we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition, and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy, our method enables the discrimination against the compression domain, and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.

关键词： Image quality computer vision Image coding pattern recognition

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Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views

Fusing Personal and Environmental Cues for Identification an...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ziwei Zhao Yuchen Wang Chuhua Wang Indiana University Bloomington

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As wearable cameras become more popular, an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet (PEN), a framework designed to integrate information from both the individuals in the two views and geometric cues inferred from the background environment. To facilitate research in this direction, we also present TF2023, a novel dataset comprising synchronized first-person and third-person views, along with masks of camera wearers and labels associating these masks with the respective first-person views. In addition, we propose a novel quantitative metric designed to measure a model's ability to comprehend the relationship between the two views. Our experiments re- veal that PEN outperforms existing methods. The code and dataset are available at https://***/ziweizhao1993/PEN.

关键词： Measurement computer vision Codes Focusing Cameras pattern recognition Synchronization

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Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes

Entity-NeRF: Detecting and Removing Moving Entities in Urban...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Takashi Otonari Satoshi Ikehata Kiyoharu Aizawa The University of Tokyo National Institute of Informatics (NII) Tokyo Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However, this approach faces challenges in modeling scene dynamics in urban environments, where moving objects of various categories and scales are present. In such settings, it becomes crucial to effectively eliminate moving objects to accurately reconstruct static backgrounds. Our research introduces an innovative method, termed here as Entity-NeRF, which combines the strengths of knowledge-based and statistical strategies. This approach utilizes entity-wise statistics, leveraging entity segmentation and stationary entity classification through thing/stuff segmentation. To assess our methodology, we created an urban scene dataset masked with moving objects. Our comprehensive experiments demonstrate that Entity-NeRF notably outperforms existing techniques in removing moving objects and reconstructing static urban backgrounds, both quantitatively and qualitatively. 1 1 Our project page is available at https://***/entitynerf/

关键词： Training computer vision Computational modeling Urban areas Knowledge based systems Neural radiance field pattern recognition

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SaCo Loss: Sample-Wise Affinity Consistency for vision-Language Pre-Training

SaCo Loss: Sample-Wise Affinity Consistency for Vision-Langu...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sitong Wu Haoru Tan Zhuotao Tian Yukang Chen Xiaojuan Qi Jiaya Jia CUHK HKU SmartMore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-language pre-training (VLP) aims to learn joint representations of vision and language modalities. The contrastive paradigm is currently dominant in this field. However, we observe a notable misalignment phenomenon, that is, the affinity between samples has an obvious disparity across different modalities, namely “Affinity Inconsistency Problem”. Our intuition is that, for a well-aligned model, two images that look similar to each other should have the same level of similarity as their corresponding texts that describe them. In this paper, we first investigate the reason of this inconsistency problem. We discover that the lack of consideration for sample-wise affinity consistency across modalities in existing training objectives is the central cause. To address this problem, we propose a novel loss function, named Sample-wise affinity Consistency (SaCo) loss, which is designed to enhance such consistency by minimizing the distance between image embedding similarity and text embedding similarity for any two samples. Our SaCo loss can be easily incorporated into existing vision-language models as an additional loss due to its complementarity for most training objectives. In addition, considering that pre-training from scratch is computationally expensive, we also provide a more efficient way to continuously pre-train on a converged model by integrating our loss. Experimentally, the model trained with our SaCo loss significantly outperforms the baseline on a variety of vision and language tasks.

关键词： Training computer vision Analytical models Computational modeling pattern recognition Computational efficiency

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Insights from the Use of Previously Unseen Neural Architecture Search Datasets

Insights from the Use of Previously Unseen Neural Architectu...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Rob Geada David Towers Matthew Forshaw Amir Atapour-Abarghouei A. Stephen McGough Newcastle University UK- The Alan Turing Institute UK Durham University UK-

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The boundless possibility of neural networks which can be used to solve a problem - each with different performance - leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However, to date, NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST, Language, MultNIST, CIFAR-Tile, Gutenberg, Isabella, GeoClassing, and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants.

关键词： Deep learning Knowledge engineering computer vision Costs Neural networks Benchmark testing pattern recognition

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IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation

IMPRINT: Generative Object Compositing by Learning Identity-...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yizhi Song Zhifei Zhang Zhe Lin Scott Cohen Brian Price Jianming Zhang Soo Ye Kim He Zhang Wei Xiong Daniel Aliaga Purdue University Adobe Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generative object compositing emerges as a promising new avenue for compositional image editing. However, the requirement of object identity preservation poses a significant challenge, limiting practical usage of most existing methods. In response, this paper introduces IMPRINT, a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic, identity-preserving pretraining of the object encoder, enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition, IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality. Project page: https://***/IMPRINT-Project-Page/

关键词： computer vision Limiting Process control pattern recognition

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PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

PANDA: Adapting Pretrained Features for Anomaly Detection an...

引用

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

作者： Reiss, Tal Cohen, Niv Bergman, Liron Hoshen, Yedid Hebrew Univ Jerusalem Sch Comp Sci & Engn Jerusalem Israel

ISBN: (纸本)9781665445092

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pre-trained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gains in anomaly detection, we adapt pre-trained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins(1).

关键词： computer vision Transfer learning Supervised learning computer architecture Performance gain Feature extraction pattern recognition

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MFP: Making Full Use of Probability Maps for Interactive Image Segmentation

MFP: Making Full Use of Probability Maps for Interactive Ima...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chaewon Lee Seon-Ho Lee Chang-Su Kim Korea University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In recent interactive segmentation algorithms, previous probability maps are used as network input to help predictions in the current segmentation round. However, despite the utilization of previous masks, useful information contained in the probability maps is not well propagated to the current predictions. In this paper, to overcome this limitation, we propose a novel and effective algorithm for click-based interactive image segmentation, called MFP, which attempts to make full use of probability maps. We first modulate previous probability maps to enhance their represen-tations of user-specified objects. Then, we feed the modulated probability maps as additional input to the segmentation network. We implement the proposed MFP algorithm based on the ResNet-34, HRNet-18, and ViT-B backbones and assess the performance extensively on various datasets. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones. The source codes are available at hups.//***/cwleetul/Ml-P.

关键词： Image segmentation computer vision Source coding Prediction algorithms pattern recognition Feeds

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Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place recognition

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descript...

引用

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

作者： Hausler, Stephen Garg, Sourav Xu, Ming Milford, Michael Fischer, Tobias Queensland Univ Technol QUT Ctr Robot Brisbane Qld Australia

ISBN: (纸本)9781665445092

Visual Place recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introduce a multi-scale fusion of patch features that have complementary scales (i.e. patch sizes) via an integral feature space and show that the fused features are highly invariant to both condition (season, structure, and illumination) and viewpoint (translation and rotation) changes. Patch-NetVLAD achieves state-of-the-art visual place recognition results in computationally limited scenarios, validated on a range of challenging real-world datasets, including winning the Facebook Mapillary Visual Place recognition Challenge at ECCV2020. It is also adaptable to user requirements, with a speed-optimised version operating over an order of magnitude faster than the state-of-the-art. By combining superior performance with improved computational efficiency in a configurable framework, Patch-NetVLAD is well suited to enhance both stand-alone place recognition capabilities and the overall performance of SLAM systems.

关键词： Visualization computer vision Simultaneous localization and mapping Social networking (online) Autonomous systems Face recognition Lighting

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