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27th International Conference on Pattern Recognition, ICPR 2024

作者： Basak, Suvramalya Vaishy, Aditya Gautam, Anjali Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad U.P. Prayagraj India

ISBN: (纸本)9783031781094

The real-world applicability of automated violence recognition systems has drawn much attention from researchers. The current techniques for recognizing violence are centered on creating efficient models that can predict violent events quickly and accurately in real-time. However, early violence prediction, which is crucial for real-time systems, is not considered in these methods. In this paper, we present an early violence prediction method that can accurately predict violent activities from partially observed video frames. We propose a two-stream architecture which employs our proposed efficient Squeeze-Excitation ShuffleNet (SESNet) model that effectively extracts spatial and temporal features. We leverage spatio-temporal and channel-wise squeeze and excitation to incorporate attention information into the ShuffleNet V2 architecture. To enable early violence recognition, we train our model in a teacher-student framework, where the teacher model trained on full-length videos distils privileged information to the student model, which has access to partial videos. For this purpose, we introduce a novel multi-teacher importance preservation learning methodology which can effectively distill important features from multiple teacher networks. We evaluate our approach on the challenging RWF-2000 public violence recognition dataset. Experimental results show that our teacher-student training framework performed well for early violence prediction. Additionally, our proposed model also outperforms several state-of-the-art violence recognition methods on full-length videos. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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PSTNet: A Progressive Sparse Transformer Network for Image Deraining 27th

PSTNet: A Progressive Sparse Transformer Network for Image ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Shaik, Raj Ahamed Singh, Navjot Verriboina, Antony Shandilya, Debesh Kumar Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Uttar Pradesh Allahabad India

ISBN: (纸本)9783031781247

Image deraining aims to transform a rainy input image into an image of high quality. Transformer-based techniques have demonstrated remarkable efficacy in image deraining because of their capacity to represent non-local information, a crucial element for high-quality image reconstruction. Our findings indicate that most transformers used for feature aggregation nowadays leverage all token similarities between query-key pairs. If the query tokens differ from the key tokens, the quality of the recovered image is compromised by the self-attention values derived from these tokens if these are considered during feature aggregation. For this purpose, we propose the Progressive Sparse Transformer Network (PSTNet), which progressively learns to restore degraded inputs by retaining the helpful self-attention information during feature aggregation and discarding the remaining values which obstruct the restoration. Transformer blocks help to capture interactions between distant pixels. Thorough experimental outcomes on widely used benchmarks show that the suggested approach performs better than most existing techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image quality

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Patch Attention Excitation Based vision Transformer for Small-Sized Datasets

Patch Attention Excitation Based Vision Transformer for Smal...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Akash Verma Shiv Ram Dubey Satish Kumar Singh Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

vision Transformers have proven their mettle across a variety of computer vision problems, however, their reliance on pretraining with very large-scale datasets such as JFT-300M is also no secret, as large amounts of data is very conductive to effective feature learning. In this paper, however, we propose a novel vision Transformer architecture PAEViT that aims to effectively learn generalized features from small-sized datasets, usually having only a few hundred images per class at most. By refining the attention scores based on patch-level interactions and modulating it to enhance the trained model’s ability to focus more on task relevant patches, PAEViT is able to significantly improve upon the performance of the regular ViT model, as well as other variants of the same, when the model is being trained solely on small-sized datasets. In data-constrained situations and visual recognition tasks that do not conform well with the existing large-scale datasets, PAEViT can be used to create effective and scalable solutions with all the features and attention scores being based only on relevant data. Our code is publicly available at https://***/AkashVermaIN/PAEViT.

关键词： Representation learning computer vision Visualization Codes Computational modeling Refining computer architecture Speech enhancement Signal processing Transformers

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Face to Cartoon Incremental Super-Resolution Using Knowledge Distillation 27th

Face to Cartoon Incremental Super-Resolution Using Knowledg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Devkatte, Trinetra Dubey, Shiv Ram Singh, Satish Kumar Hadid, Abdenour Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad Allahabad India Sorbonne Centre for Artificial Intelligence Sorbonne University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781827

Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt to new, unseen data remains a challenge. This paper addresses this problem by proposing an incremental super-resolution using GANs with knowledge distillation (ISR-KD) for face to cartoon. Previous research in this area has not investigated incremental learning, which is critical for real-world applications where new data is continually being generated. The proposed ISR-KD aims to develop a novel unified framework for facial super-resolution that can handle different settings, including different types of faces such as cartoon face and various levels of detail. To achieve this, a GAN-based super-resolution network was pre-trained on the CelebA dataset and then incrementally trained on the iCartoonFace dataset, using knowledge distillation to retain performance on the CelebA test set while improving the performance on iCartoonFace test set. Our experiments demonstrate the effectiveness of knowledge distillation in incrementally adding capability to the model for cartoon face super-resolution while retaining the learned knowledge for facial hallucination tasks in GANs. The code is released at https://***/TRINETRA-DEVKATTE/ISR-KD-Network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Patch Attention Excitation Based vision Transformer for Small-Sized Datasets

Patch Attention Excitation Based Vision Transformer for Smal...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Verma, Akash Dubey, Shiv Ram Singh, Satish Kumar Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India

ISBN: (纸本)9798350368741

vision Transformers have proven their mettle across a variety of computer vision problems, however, their reliance on pretraining with very large-scale datasets such as JFT-300M is also no secret, as large amounts of data is very conductive to effective feature learning. In this paper, however, we propose a novel vision Transformer architecture PAEViT that aims to effectively learn generalized features from small-sized datasets, usually having only a few hundred images per class at most. By refining the attention scores based on patch-level interactions and modulating it to enhance the trained model's ability to focus more on task relevant patches, PAEViT is able to significantly improve upon the performance of the regular ViT model, as well as other variants of the same, when the model is being trained solely on small-sized datasets. In data-constrained situations and visual recognition tasks that do not conform well with the existing large-scale datasets, PAEViT can be used to create effective and scalable solutions with all the features and attention scores being based only on relevant data. Our code is publicly available at https://***/AkashVermaIN/PAEViT. © 2025 IEEE.

关键词： Attention Mechanisms Data Efficiency Patch Attention vision Transformers

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran Indian Institute of Information Technology Allahabad Computer Vision and Biometrics Lab Department of Information Technology Uttar Pradesh Prayagraj211015 India University of South Dakota AI Research Lab Department of Computer Science VermillionSD57069 United States Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as 'attacks,' underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, CalTech256 and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3 and efficientNet b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique. © 2020 IEEE.

关键词： Convolutional neural networks

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Small object detection in diverse application landscapes: a survey

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Multimedia Tools and Applications 2024年第41期83卷 88645-88680页

作者： Iqra Giri, Kaisar J. Javed, Mohammed Department of Computer Science Islamic University of Science & Technology Pulwama India Computer Vision & Biometrics Lab Dept. of IT Indian Institute of Information Technology Allahabad India

The importance of object detection within computer vision, especially in the context of detecting small objects, has notably increased. This thorough survey extensively examines small object detection across various applications, consolidating and outlining the available methodologies. Traditional papers on small object detection have focused on specific domains. However, this survey paper incorporates insights from a multitude of domains, providing a comprehensive understanding of the versatility and applicability of small object detection techniques. This paper sheds light on the key challenges faced and delves into potential solutions to address the challenges, offering insights into viable solutions to enhance small object detection performance, setting it apart from existing literature. The strategies identified in our survey encompass a spectrum of approaches, categorized as transformer-based, CNN, and traditional methods. Also, this paper collates prevalent datasets relevant to small object detection, simplifying access to these resources. Further, it provides a succinct overview of diverse evaluation metrics used for performance assessment in this field, enhancing understanding of the effectiveness and proficiency of these methods. This survey paper not only consolidates established knowledge but also highlights innovative viewpoints, providing a comprehensive and enlightening compilation that contributes to the advancement of small object detection in the field of computer vision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Eye-verifier using ternary template for reliable eye detection in facial color images

Eye-verifier using ternary template for reliable eye detecti...

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IEEE 3rd International Conference on biometrics: Theory, Applications and Systems, BTAS 2009

作者： Van Huan, Nguyen Binh, Nguyen Thi Hai Kim, Hakil Computer Vision Lab. INHA University Incheon Korea Republic of Biometrics Engineering Research Center Korea Republic of

ISBN: (纸本)9781424450206

This paper introduces an eye-verifier for reliable detection of eyes in facial color images. At first, the eye region candidates are searched by the circular filter to the binary face image. The eye candidates are then fed into an eye-verifier. The eye-verifier uses a ternary template, generated from the eye area consisting of iris, sclera and skin. Then the template matching is made by ternary Hamming distance. Experimental results evaluating the proposed method on our own face database and FERET database show promising performance in terms of detection rate. The proposed algorithm showed a robust performance to the face poses and eye directions. ©2009 IEEE.

关键词： Template matching

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Semantic Map Injected GAN Training for Image-to-Image Translation 12th

Semantic Map Injected GAN Training for Image-to-Image Transl...

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12th Indian Conference on computer vision, Graphics, and Image Processing, ICVGIP 2021

作者： Kshatriya, Balaram Singh Dubey, Shiv Ram Sarma, Himangshu Chaudhary, Kunal Gurjar, Meva Ram Rai, Rahul Manchanda, Sunny Computer Vision Group Indian Institute of Information Technology Sri City Chittoor India Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India Bangalore India

ISBN: (纸本)9789811941351

Image-to-image translation is the recent trend to transform images from one domain to another domain using a generative adversarial network (GAN). The existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Let us refer to the original training as the training for the translation of the input image into the target domain. The injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. The semantic map is only utilized at the training time and is not required at the test time. The experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID, and KID scores after injecting semantic training as compared to the original training. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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computer vision and Machine Intelligence 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Massimo Tistarelli Shiv Ram Dubey Satish Kumar Singh Xiaoyi Jiang

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