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ALINA: Advanced Line Identification and Notation Algorithm

ALINA: Advanced Line Identification and Notation Algorithm

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohammed Abdul Hafeez Khan Parth Ganeriwala Siddhartha Bhattacharyya Natasha Neogi Raja Muthalagu Florida Institute of Technology Melbourne Florida NASA Langley Research Center Hampton Virginia BITS Pilani Dubai Campus Dubai UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

关键词： Visualization Costs Annotations Image edge detection Roads Manuals pattern recognition

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A Guided Multi-Scale Categorization of Plant Species in Natural Images

A Guided Multi-Scale Categorization of Plant Species in Natu...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jonas Krause Kyungim Baek Lipyeow Lim Dept. of Information and Computer Sciences University of Hawai'i at Manoa Honolulu HI

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Automatic categorization of plant species in natural images is an important computer vision problem with numerous applications in agriculture and botany. The problem is particularly challenging due to the large number of plant species, the inter-species similarity, the large scale variations in natural images, and the lack of annotated data. In this paper, we present a guided multi-scale approach that segments the regions of interest (containing a plant) from a complex background of the natural image and systematically extracts scale-representative patches based on those regions. These multi-scale patches are used to train state-of-the-art Convolutional Neural Network (CNN) models that analyze a given plant image and determine its species. Focusing specifically on the identification of plant species in natural images, we show that the proposed approach is a very effective way of making deep learning models more robust to scale variations. We perform a comprehensive experimental evaluation of our proposed method over several CNN models. Our best result on the Inception-ResNet-v2 model achieves a top-1 classification accuracy of 89.21% for 100 plant species which represents a 5.4% increase over using random cropping to generate training data.

关键词： Image segmentation Birds Feature extraction Biological system modeling Streaming media computer vision Head

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

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Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

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What does CLIP know about peeling a banana?

What does CLIP know about peeling a banana?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Claudia Cuttano Gabriele Rosi Gabriele Trivigno Giuseppe Averta Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Humans show an innate capability to identify tools to support specific actions. The association between objects parts and the actions they facilitate is usually named affordance. Being able to segment objects parts depending on the tasks they afford is crucial to enable intelligent robots to use objects of daily living. Traditional supervised learning methods for affordance segmentation require costly pixel-level annotations, while weakly supervised approaches, though less demanding, still rely on object-interaction examples and support a closed set of actions. These limitations hinder scalability, may introduce biases, and usually restrict models to a limited set of predefined actions. This paper proposes Affordance-CLIP, to overcome these limitations by leveraging the implicit affordance knowledge embedded within large pre-trained vision-Language models like CLIP. We experimentally demonstrate that CLIP, although not explicitly trained for affordances detection, retains valuable information for the task. Our AffordanceCLIP achieves competitive zero-shot performance compared to methods with specialized training, while offering several advantages: i) it works with any action prompt, not just a predefined set; ii) it requires training only a small number of additional parameters compared to existing solutions and iii) eliminates the need for direct supervision on action-object pairs, opening new perspectives for functionality-based reasoning of models.

关键词： Training computer vision Affordances Scalability conferences Computational modeling Supervised learning

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DOAD: Decoupled One Stage Action Detection Network

DOAD: Decoupled One Stage Action Detection Network

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shuning Chang Pichao Wang Fan Wang Jiashi Feng Mike Zheng Shou Showlab National University of Singapore Alibaba Group National University of Singapore

ISBN: (数字)9798350302493

ISBN: (纸本)9798350302509

Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. However, such two-stage methods are generally with low efficiency. We observe that directly unifying detection and action recognition normally suffers from (i) inferior learning due to different desired properties of context representation for detection and action recognition; (ii) optimization difficulty with insufficient training data. In this work, we present a decoupled onestage network dubbed DOAD, to mitigate above issues and improve the efficiency for spatio-temporal action detection. To achieve it, we decouple detection and action recognition into two branches. Specifically, one branch focuses on detection representation for actor detection, and the other one for action recognition. For the action branch, we design a transformer-based module (TransPC) to model pairwise relationships between people and context. Different from commonly used vector-based dot product in self-attention, it is built upon a novel matrix-based key and value for Hadamard attention to model person-context information. It not only exploits relationships between person pairs but also takes into account context and relative position information. The results on AVA and UCF101-24 datasets show that our method is competitive with two-stage state-of-theart methods with significant efficiency improvement.

关键词： computer vision conferences Training data Transformers Feature extraction pattern recognition Task analysis

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ren Yang Radu Timofte Meisong Zheng Qunliang Xing Minglang Qiao Mai Xu Lai Jiang Huaida Liu Ying Chen Youcheng Ben Xiao Zhou Chen Fu Pei Cheng Gang Yu Junyi Li Renlong Wu Zhilu Zhang Wei Shang Zhengyao Lv Yunjin Chen Mingcai Zhou Dongwei Ren Kai Zhang Wangmeng Zuo Pavel Ostyakov Vyal Dmitry Shakarim Soltanayev Chervontsev Sergey Zhussip Magauiya Xueyi Zou Youliang Yan Pablo Navarrete Michelini Yunhua Lu Diankai Zhang Shaoli Liu Si Gao Biao Wu Chengjian Zheng Xiaofeng Zhang Kaidi Lu Ning Wang Thuong Nguyen Canh Thong Bach Qing Wang Xiaopeng Sun Haoyu Ma Shijie Zhao Junlin Li Liangbin Xie Shuwei Shi Yujiu Yang Xintao Wang Jinjin Gu Chao Dong Xiaodi Shi Chunmei Nian Dong Jiang Jucai Lin Zhihuai Xie Mao Ye Dengyan Luo Liuhan Peng Shengjie Chen Xin Liu Qian Wang Boyang Liang Hang Dong Yuhao Huang Kai Chen Xingbei Guo Yujing Sun Huilei Wu Pengxu Wei Yulin Huang Junying Chen Ik Hyun Lee Sunder Ali Khowaja Jiseok Yoon ETH Z&#x00FC rich rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://***/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol

The devil is in discretization discrepancy. Robustifying Dif...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Konstanty Subbotko Wojciech Jablonski Piotr Bilinski University of Warsaw

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process of obtaining the final architecture. In our work, we first study the risk of discretization error and show how it affects an unregularized supernet. Then, we present that penalizing high entropy, a common technique of architecture regularization, can hinder the supernet’s performance. Therefore, to robustify the DNAS framework, we introduce a novel single-stage searching protocol, which is not reliant on decoding a continuous architecture. Our results demonstrate that this approach outperforms other DNAS methods by achieving 75.3% in the searching stage on the Cityscapes validation dataset and attains performance 1.1% higher than the optimal network of DCNAS on the non-dense search space comprising short connections. The entire training process takes only 5.5 GPU days due to the weight reuse, and yields a computationally efficient architecture. Additionally, we propose a new dataset split procedure, which substantially improves results and prevents architecture degeneration in DARTS.

关键词： Training computer vision Protocols Neural networks DNA Graphics processing units computer architecture

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Using Mixture of Expert Models to Gain Insights into Semantic Segmentation

Using Mixture of Expert Models to Gain Insights into Semanti...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Svetlana Pavlitskaya Christian Hubschneider Michael Weber Ruby Moritz Fabian Hüger Peter Schlicht J. Marius Zöllner FZI Research Center for Information Technology Volkswagen Group Automation

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architecture to achieve additional interpretability while retaining comparable result quality. By being able to use both the overall model output as well as retaining the possibility to take into account individual expert outputs, the agreement or disagreement between those individual outputs can be used to gain insights into the decision process. Expert networks are trained by splitting the input data into semantic subsets, e.g. corresponding to different driving scenarios, to become experts in those domains. An additional gating network that is also trained on the same input data is consequently used to weight the output of individual experts. We evaluate this mixture of expert setup on the A2D2 dataset and achieve similar results to a baseline FRRN network trained on all available data, while getting additional information.

关键词： Logic gates computer architecture Feature extraction Uncertainty Semantics Neural networks Task analysis

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ViTKD: Feature-based Knowledge Distillation for vision Transformers

ViTKD: Feature-based Knowledge Distillation for Vision Trans...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhendong Yang Zhe Li Ailing Zeng Zexian Li Chun Yuan Yu Li Tsinghua Shenzhen International Graduate School International Digital Economy Academy (IDEA) Institute of Automation Chinese Academy of Sciences Beihang University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Knowledge Distillation (KD) has been extensively studied as a means to enhance the performance of smaller models in Convolutional Neural Networks (CNNs). Recently, the vision Transformer (ViT) has demonstrated remarkable success in various computer vision tasks, leading to an increased demand for KD in ViT. However, while logit-based KD has been applied to ViT, other feature-based KD methods for CNNs cannot be directly implemented due to the significant structure gap. In this paper, we conduct an analysis of the properties of different feature layers in ViT to identify a method for feature-based ViT distillation. Our findings reveal that both shallow and deep layers in ViT are equally important for distillation and require distinct distillation strategies. Based on these guidelines, we propose our feature-based method ViTKD, which mimics the shallow layers and generates the deep layer in the teacher. ViTKD leads to consistent and significant improvements in the students. On ImageNet-1K, we achieve performance boosts of 1.64% for DeiT-Tiny, 1.40% for DeiT-Small, and 1.70% for DeiT-Base. Downstream tasks also demonstrate the superiority of ViTKD. Additionally, ViTKD and logit-based KD are complementary and can be applied together directly, further enhancing the student’s performance. Specifically, DeiT-T, S, and B achieve accuracies of 77.78%, 83.59%, and 85.41%, respectively, using this combined approach. Code is available at https://***/yzd-v/cls_KD.

关键词： Knowledge engineering computer vision conferences Computational modeling MIMICs Transformers pattern recognition

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