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Context-Based Trit-Plane Coding for Progressive Image Compression

Context-Based Trit-Plane Coding for Progressive Image Compre...

引用

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

作者： Jeon, Seungmin Choi, Kwang Pyo Park, Youngo Kim, Chang-Su Korea Univ Seoul South Korea Samsung Elect Seoul South Korea

ISBN: (纸本)9798350301298

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly, e.g. by -14.84% in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. The source codes are available at https://***/seungminjeon-github/CTC.

关键词： Low-level vision

来源：评论

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Test of Time: Instilling Video-Language Models with a Sense of Time

Test of Time: Instilling Video-Language Models with a Sense ...

引用

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

作者： Bagad, Piyush Tapaswi, Makarand Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798350301298

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.

关键词： language reasoning vision

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Simulating Task-Free Continual Learning Streams from Existing Datasets

Simulating Task-Free Continual Learning Streams from Existin...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Chrysakis, Aristotelis Moens, Marie-Francine KU Leuven Department of Computer Science Leuven Belgium

ISBN: (纸本)9798350302493

Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continual learning using streams with distributions that change not continuously, but only at a few distinct points in time. In order to address the discrepancy between the definition and evaluation of task-free continual learning, we propose a principled algorithm that can permute any labeled dataset into a stream that is continuously nonstationary. We empirically show that the streams generated by our algorithm are less structured than the ones conventionally used in the literature. Moreover, we use our simulated task-free streams to benchmark multiple methods applicable to the task-free setting. We hope that our work will allow other researchers to better evaluate learning performance on continuously nonstationary streams. © 2023 ieee.

关键词： Learning systems

来源：评论

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Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System

Robust Automatic Motorcycle Helmet Violation Detection for a...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Tran, Duong Nguyen-Ngoc Hoang Pham, Long Jeon, Hyung-Joon Nguyen, Huy-Hung Jeon, Hyung-Min Tran, Tai Huu-Phuong Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350302493

Video surveillance-based automatic detection of motorcycle helmet usage can enhance the effectiveness of educational and enforcement initiatives aimed at boosting road safety. Current detection methods, however, have room for enhancement, such as the inability to pinpoint individual motorcycles or differentiate between drivers and passengers in terms of helmet usage. This paper introduces a framework designed to detect and identify individual motorcycles while recording specific helmet usage for riders. The proposed classification approach for helmet usage demonstrates increased efficiency in comparison to previous research. Our findings highlight the exceptional accuracy of deep learning, with our method achieving a score of 0.7754 on the AI City 2023 Challenge Track 5 public leaderboard. © 2023 ieee.

关键词： Motorcycles

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Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Humblot-Renaux, Galadrielle Escalera, Sergio Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Denmark Universitat Autònoma de Barcelona Computer Vision Center Spain Universitat de Barcelona Dept. of Mathematics and Informatics Spain

ISBN: (纸本)9798350302493

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc © 2023 ieee.

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NVTC: Nonlinear Vector Transform Coding

NVTC: Nonlinear Vector Transform Coding

引用

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

作者： Feng, Runsen Guo, Zongyu Li, Weiping Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (RD) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://***/USTC-IMCL/NVTC.

关键词： Low-level vision

来源：评论

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Data-driven Feature Tracking for Event Cameras

Data-driven Feature Tracking for Event Cameras

引用

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

作者： Messikommer, Nico Fang, Carter Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.

关键词： Low-level vision

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Automatic High Resolution Wire Segmentation and Removal

Automatic High Resolution Wire Segmentation and Removal

引用

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

作者： Chiu, Mang Tik Zhang, Xuaner Wei, Zijun Zhou, Yuqian Shechtman, Eli Barnes, Connelly Lin, Zhe Kainz, Florian Amirghodsi, Sohrab Shi, Humphrey UIUC Champaign IL 61801 USA Adobe San Jose CA USA Univ Oregon Eugene OR USA

ISBN: (纸本)9798350301298

Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local contexts to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal with great generalization to various wire appearances.

关键词： vision applications and systems

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SViTT: Temporal Learning of Sparse Video-Text Transformers

SViTT: Temporal Learning of Sparse Video-Text Transformers

引用

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

作者： Li, Yi Min, Kyle Tripathi, Subarna Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9798350301298

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size;the curse of dimensionality for multi-frame modeling;and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://***/projects/svitt.

关键词： and reasoning language vision

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Asynchronous Federated Continual Learning

Asynchronous Federated Continual Learning

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Shenaj, Donald Toldo, Marco Rigon, Alberto Zanuttigh, Pietro University of Padova Italy

ISBN: (纸本)9798350302493

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://***/LTTM/FedSpace. © 2023 ieee.

关键词： computer aided instruction

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