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The revenge of BiSeNet: Efficient Multi-Task Image Segmentation

The revenge of BiSeNet: Efficient Multi-Task Image Segmentat...

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

作者： Gabriele Rosi Claudia Cuttano Niccolò Cavagnero Giuseppe Averta Fabio Cermelli Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices. However, existing research has primarily concentrated on single-task settings, especially on semantic segmentation, leading to redundant efforts and specialized architectures for different tasks. To address this limitation, we propose a novel architecture for efficient multi-task image segmentation, capable of handling various segmentation tasks without sacrificing efficiency or accuracy. We introduce BiSeNetFormer, that leverages the efficiency of two-stream semantic segmentation architectures and it extends them into a mask classification framework. Our approach maintains the efficient spatial and context paths to capture detailed and semantic information, respectively, while leveraging an efficient transformed-based segmentation head that computes the binary masks and class probabilities. By seamlessly supporting multiple tasks, namely semantic and panoptic segmentation, BiSeNetFormer offers a versatile solution for multi-task segmentation. We evaluate our approach on popular datasets, Cityscapes and ADE20K, demonstrating impressive inference speeds while maintaining competitive accuracy compared to state-of-the-art architectures. Our results indicate that BiSeNetFormer represents a significant advancement towards fast, efficient, and multi-task segmentation networks, bridging the gap between model efficiency and task adaptability.

关键词： computer vision Accuracy Semantic segmentation Semantics Refining Redundancy computer architecture

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HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classif...

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

作者： William Michael Laprade Pawel Pieta Svetlana Kutuzova Jesper Cairo Westergaard Mads Nielsen Svend Christensen Anders Bjorholm Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Computer Science University of Copenhagen Department of Plant and Environmental Sciences University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis.

关键词： Adaptation models Image analysis Image coding Surveillance conferences Focusing Distance measurement

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Salient Object-Aware Background Generation using Text-Guided Diffusion Models

Salient Object-Aware Background Generation using Text-Guided...

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

作者： Amir Erfan Eshratifar João V. B. Soares Kapil Thadani Shaunak Mishra Mikhail Kuznetsov Yueh-Ning Ku Paloma De Juan Yahoo Research Amazon ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments. Background generation can be framed as a task of text-conditioned outpainting, where the goal is to extend image content beyond a salient object’s boundaries on a blank background. Although popular diffusion models for text-guided inpainting can also be used for outpainting by mask inversion, they are trained to fill in missing parts of an image rather than to place an object into a scene. Consequently, when used for background creation, inpainting models frequently extend the salient object’s boundaries and thereby change the object’s identity, which is a phenomenon we call "object expansion." This paper introduces a model for adapting inpainting diffusion models to the salient object outpainting task using Stable Diffusion and ControlNet architectures. We present a series of qualitative and quantitative results across models and datasets, including a newly proposed metric to measure object expansion that does not require any human labeling. Compared to Stable Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by 3.6× on average with no degradation in standard visual metrics across multiple datasets.

关键词： Measurement Degradation Adaptation models Visualization conferences Diffusion models pattern recognition

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MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers

MA-AVT: Modality Alignment for Parameter-Efficient Audio-Vis...

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

作者： Tanvir Mahmud Shentong Mo Yapeng Tian Diana Marculescu University of Texas at Austin Carnegie Mellon University University of Texas at Dallas

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advances in pre-trained vision transformers have shown promise in parameter-efficient audio-visual learning without audio pre-training. However, few studies have investigated effective methods for aligning multimodal features in parameter-efficient audio-visual transformers. In this paper, we propose MA-AVT, a new parameter-efficient audio-visual transformer employing deep modality alignment for corresponding multimodal semantic features. Specifically, we introduce joint unimodal and multimodal token learning for aligning the two modalities with a frozen modality-shared transformer. This allows the model to learn separate representations for each modality, while also attending to the cross-modal relationships between them. In addition, unlike prior work that only aligns coarse features from the output of unimodal encoders, we introduce blockwise contrastive learning to align coarse-to-fine-grain hierarchical features throughout the encoding phase. Furthermore, to suppress the background features in each modality from foreground matched audio-visual features, we introduce a robust discriminative foreground mining scheme. Through extensive experiments on benchmark AVE, VGGSound, and CREMA-D datasets, we achieve considerable performance improvements over SOTA methods.

关键词： Training computer vision conferences Semantics Contrastive learning Benchmark testing Transformers

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

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

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： computer vision Visualization conferences Crops Cameras Trajectory pattern recognition

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Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid

Blurry-Consistency Segmentation Framework with Selective Sta...

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

作者： Thanh-Huy Nguyen Thi Kim Ngan Ngo Mai Anh Vu Ting-Yuan Tu Department of Biomedical Engineering IMBSL Lab National Cheng Kung University Tainan ROC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model’s stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.

关键词： Training Solid modeling Three-dimensional displays Microscopy Stacking computer architecture Breast cancer

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Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

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Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

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

作者： Haoyu Zhang Raghavendra Ramachandra Kiran Raja Christoph Busch Norwegian University of Science and Technology Norway Darmstadt University of Applied Sciences Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face morphing attacks have posed severe threats to Face recognition Systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Training computer vision Image coding Protocols Statistical analysis Face recognition Digital images

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NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel...

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

作者： Abuolaim, Abdullah Timofte, Radu Brown, Michael S. Zhang, Dafeng Wang, Xiaobing Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Liu, Shuai Lei, Lei Feng, Chaoyu Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Zhu, Yunan Liu, Dong Vo, Tu Miao, Si Shah, Nisarg A. Liang, Pengwei Zhong, Zhiwei Hu, Xingyu Chen, Yiqun Li, Chenghua Bai, Xiaoying Zhang, Chi Yao, Yiheng Gang, Ruipeng Nathan, Sabari Ragavendran, Thangavelu Srinija, Venkatakrishnan Srivatsav, Venkatakrishnan York Univ N York ON Canada Swiss Fed Inst Technol Zurich Switzerland Samsung Res China Beijing SRC B Beijing Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates North China Univ Technol Beijing Peoples R China Xiaomi Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Bridge AI Inc Seongnam South Korea Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Indian Inst Technol Jodhpur Karwar Rajasthan India Harbin Inst Technol Harbin Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Meerut Uttar Pradesh India Jeppair Coll Engn Chennai Tamil Nadu India Sri Sai Ram Inst Technol Chennai Tamil Nadu India Prince Shri Venkateshwara Padmavathy Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781665448994

This paper provides a review of the NTIRE 2021 challenge targeting defocus deblurring using dual-pixel (DP) data. The goal of this single-track challenge was to reduce spatially varying defocus blur present in images captured with a shallow depth of field. The images used in this challenge were obtained using a DP sensor that provided a pair of DP views per captured image. Submitted solutions were evaluated using conventional signal processing metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Out of 185 registered participants, nine teams provided methods and competed in the final stage. The paper describes the methods proposed by the participating teams and their results. The winning teams represent the state-of-the-art in terms of defocus deblurring using DP images.

关键词： computer vision PSNR Atmospheric measurements conferences Signal processing Particle measurements pattern recognition

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T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification

T2FNorm: Train-time Feature Normalization for OOD Detection ...

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

作者： Sudarshan Regmi Bibek Panthi Sakar Dotel Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal Tribhuvan University Nepal West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

关键词： Training Measurement Deep learning computer vision Accuracy conferences Neural networks

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