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Continual Learning Based on OOD Detection and Task Masking

Continual Learning Based on OOD Detection and Task Masking

引用

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

作者： Kim, Gyuhak Esmaeilpour, Sepideh Xiao, Changnan Liu, Bing Univ Illinois Chicago IL 60607 USA ByteDance Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unified approach based on out-of-distribution (OOD) detection and task masking, called CLOM, to solve both problems. The key novelty is that each task is trained as an OOD detection model rather than a traditional supervised learning model, and a task mask is trained to protect each task to prevent forgetting. Our evaluation shows that CLOM outperforms existing state-of-the-art baselines by large margins. The average TIL/CIL accuracy of CLOM over six experiments is 87.6/67.9% while that of the best baselines is only 84.4/55.0%.

关键词： Training computer vision Machine learning algorithms Codes conferences Supervised learning Data models

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Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

Wearable ImageNet: Synthesizing Tileable Textures via Datase...

引用

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent methods for Dataset Distillation are able to take in a large set of images of a specific class (e.g., from ImageNet) and synthesize a single image, such that a classifier trained on that image could perform similarly to one trained on the original dataset. It was noticed that the resulting "distilled images" are often quite visually pleasing. In this paper, we describe a simple method for generating tileable distilled textures by sampling random crops from a toroidal canvas of synthetic pixels while enforcing that all such crops serve as effective distilled training data. Such distilled textures not only summarize a given image category in a visually interesting way, but also allow for generation of infinite texture patterns suitable for printing on fabric, clothing, etc. This paper might be just the first step in making the ImageNet dataset into a fashion statement.

关键词： Printing computer vision Wearable computers conferences Clothing Crops Training data

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Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture

Frequency Decoupling for Motion Magnification via Multi-Leve...

引用

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

作者： Wang, Fei Guo, Dan Li, Kun Zhong, Zhun Wang, Meng Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9798350353006

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observe that the low-frequency components with stable energy always possess spatial structure and less noise, making them suitable for modeling the subtle motion field. To this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degradation due to their inherent subtlety and unavoidable external interference from noise, we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures, and a Sparse Frequency Mixer to promote seamless recoupling. Besides, we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features, reducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63x and boosts inference speed by 1.68x than the latest method. Our code is available at https://***/Jiafei127/FD4MM.

关键词： computer vision Frequency Decoupling Video Motion Magnification

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Can we trust bounding box annotations for object detection?

Can we trust bounding box annotations for object detection?

引用

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

作者： Murrugarra-Llerena, Jeffri Kirsten, L. N. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is a classical problem in computer vision, and the vast majority of approaches require large annotated datasets for training and evaluation purposes. The most popular representations are bounding boxes (BBs), usually defined as the minimal-area rectangle that encompasses the whole object region. However, the annotation process presents some subjectiveness (particularly when occlusions are present), and its quality might get degraded when the annotators get tired. Comparing BBs is crucial for evaluation purposes, and the Intersection-over-Union (IoU) is the standard similarity metric. In this paper, we provide theoretical and experimental results indicating that the IoU can be strongly affected even by small annotation discrepancies in popular datasets used for object detection. As a consequence, the Average Precision (AP) value commonly used to evaluate object detectors is also influenced by annotation bias or noise, particularly for small objects and tighter IoU thresholds.

关键词： Degradation Training computer vision Annotations Object detection Detectors Size measurement

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

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

作者： Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9781665487399

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Und...

引用

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

作者： Chen, Sijin Chen, Xin Zhang, Chi Li, Mingsheng Yu, Gang Fei, Hao Zhu, Hongyuan Fan, Jiayuan Chen, Tao Fudan Univ Shanghai Peoples R China Tencent PCG Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore ASTAR Inst InfoComm Res I2R Singapore Singapore ASTAR Ctr Frontier AI Res CFAR Singapore Singapore

ISBN: (纸本)9798350353006

Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions. However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud representations of the 3D scene. Existing works seek help from multi-view images by projecting 2D features to 3D space, which inevitably leads to huge computational overhead and performance degradation. In this paper, we present LL3DA, a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions. The additional visual interaction enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts. Experiments show that LL3DA achieves remarkable results and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering.

关键词： large language models Multi-modal learning vision and language

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Multiple Object Detection and Tracking in the Thermal Spectrum

Multiple Object Detection and Tracking in the Thermal Spectr...

引用

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

作者： El Ahmar, Wassim A. Kolhatkar, Dhanvin Nowruzi, Farzan Erlik AlGhamdi, Hamzah Hou, Jonathan Laganiere, Robert Univ Ottawa Ottawa ON Canada Sensor Cortek Inc Ottawa ON Canada Pleora Technol Kanata ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applications, it is necessary to utilize sensors that operate in different spectrums to accommodate for varying lighting conditions. Since object detection is the first step of the tracking pipeline in tracking-by-detection approaches, we compare the performance of state-of-the-art object detectors when trained on color images to their performance when trained on thermal images. We introduce a new dataset for multiple object tracking with thermal images and corresponding RGB images and show that state-of-the-art trackers perform better on thermal images, especially in poor lighting conditions. Finally, we propose the use of a dynamic cut-off threshold for tracking-by-detection approaches that factors the size of a predicted box to enhance the tracker association. Our dataset and source code are publicly available at https://***/wassimea/thermalMOT

关键词： Machine vision conferences Pipelines Lighting Object detection Detectors Streaming media

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ResNeSt: Split-Attention Networks

ResNeSt: Split-Attention Networks

引用

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

作者： Zhang, Hang Wu, Chongruo Zhang, Zhongyue Zhu, Yi Lin, Haibin Zhang, Zhi Sun, Yue He, Tong Mueller, Jonas Manmatha, R. Li, Mu Smola, Alexander Meta Menlo Pk CA 94025 USA Univ Calif Davis Davis CA USA Snap Santa Monica CA USA Amazon Seattle WA USA ByteDance Beijing Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off.

关键词： Deep learning computer vision Computational modeling conferences computer architecture pattern recognition Convolutional neural networks

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Rethinking Adversarial Examples in Wargames

Rethinking Adversarial Examples in Wargames

引用

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

作者： Chen, Yuwei Chinese Aeronaut Estab Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Artificial intelligence technology is increasingly widely used in games, especially for wargames. The addition of artificial intelligence algorithms enables these games to solve decision-making problems in complex environments more quickly, surpassing the vast majority of experienced human players in competitive games. However, due to the vulnerability of the neural network before adversarial examples, all modules using artificial intelligence algorithms are at risk of being attacked. For wargames, adversarial examples will make the units in the game no longer able to follow the established routes or actions to perform tasks. Based on such risks, this paper proposes a deceptive concept scheme of attacking intelligent modules in wargames through adversarial examples, and proposes challenges and prospects for current technologies. To our knowledge, we are the first team to analyze the impact of adversarial examples in the running process of wargames, namely the OODA loop, and simulate them in the corresponding wargaming software. In the end, we found that when artificial intelligence technology is widely used in war games, adversarial examples will have a subversive impact on several activities in several steps, which will directly lead to the failure to complete the established game goals.

关键词： Neural networks Decision making Games Prediction algorithms Software pattern recognition Security

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BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in vision-based Roadside 3D Object Detection

BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Represen...

引用

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

作者： Wang, Wenjie Lu, Yehao Zheng, Guangcong Zhan, Shuigen Ye, Xiaoqing Tan, Zichang Wang, Jingdong Wang, Gaoang Li, Xi Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Polytech Inst Hangzhou Zhejiang Peoples R China Baidu Beijing Peoples R China Zhejiang Singapore Innovat & AI Joint Res Lab Singapore Singapore

ISBN: (纸本)9798350353006

vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain, since it encompasses inherent advantages in reducing blind spots and expanding perception range. While previous work mainly focuses on accurately estimating depth or height for 2D- to-3D mapping, ignoring the position approximation error in the voxel pooling process. Inspired by this insight, we propose a novel voxel pooling strategy to reduce such error, dubbed BEVSpread. Specifically, instead of bringing the image features contained in a frustum point to a single BEV grid, BEVSpread considers each frustum point as a source and spreads the image features to the surrounding BEV grids with adaptive weights. To achieve superior propagation performance, a specific weight function is designed to dynamically control the decay speed of the weights according to distance and depth. Aided by customized CUDA parallel acceleration, BEVSpread achieves comparable inference time as the original voxel pooling. Extensive experiments on two large-scale roadside benchmarks demonstrate that, as a plug-in, BEVSpread can significantly improve the performance of existing frustum-based BEV methods by a large margin of (1.12, 5.26, 3.01) AP in vehicle, pedestrian and cyclist. The source code will be made publicly available at BEVSpread.

关键词： 3D Object Detection Autonomous Driving BEV

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