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TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

TransER: Hybrid Model and Ensemble-based Sequential Learning...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Trung Hoang Haichuan Zhang Amirsaeed Yazdani Vishal Monga School of Electrical Engineering and Computer Science Pennsylvania State University Unversity Park USA

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://***/trungpsu1210/TransER.

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MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture

MTLSegFormer: Multi-task Learning with Transformers for Sema...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Diogo Nunes Goncalves Jose Marcato Pedro Zamboni Hemerson Pistori Jonathan Li Keiller Nogueira Wesley Nunes Goncalves Faculty of Computer Science Federal University of Mato Grosso do Sul MS Brazil Faculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul MS Brazil INOVISAO Dom Bosco Catholic University MS Brazil Department of Geography and Environmental Management University of Waterloo Waterloo Ontario Canada University of Stirling Stirling Scotland UK

Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between the branches usually occurs through the concatenation or sum of the feature maps of the branches. However, this type of information exchange does not directly consider the local characteristics of the image nor the level of importance or correlation between the tasks. In this paper, we propose a semantic segmentation method, MTLSegFormer, which combines multi-task learning and attention mechanisms. After the backbone feature extraction, two feature maps are learned for each task. The first map is proposed to learn features related to its task, while the second map is obtained by applying learned visual attention to locally re-weigh the feature maps of the other tasks. In this way, weights are assigned to local regions of the image of other tasks that have greater importance for the specific task. Finally, the two maps are combined and used to solve a task. We tested the performance in two challenging problems with correlated tasks and observed a significant improvement in accuracy, mainly in tasks with high dependence on the others.

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Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach

Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly G...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ayush K. Rai Tarun Krishna Feiyan Hu Alexandru Drimbarean Kevin McGuinness Alan F. Smeaton Noel E. O’Connor Insight SFI Centre for Data Analytics Dublin City University Tobii Corporation Galway

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances. Recent works have investigated the creation of pseudo-anomalies (PAs) using only the normal data and making strong assumptions about real-world anomalies with regards to abnormality of objects and speed of motion to inject prior information about anomalies in an autoencoder (AE) based reconstruction model during training. This work proposes a novel method for generating generic spatio-temporal PAs by inpainting a masked out region of an image using a pre-trained Latent Diffusion Model and further perturbing the optical flow using mixup to emulate spatio-temporal distortions in the data. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting by learning three types of anomaly indicators, namely reconstruction quality, temporal irregularity and semantic inconsistency. Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue, ShanghaiTech and UBnormal demonstrate the effectiveness of our work against other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting. Our analysis also examines the transferability and generalisation of PAs across these datasets, offering valuable insights by identifying real-world anomalies through PAs. Our results can be reproduced on github.

关键词： Training Semantics Training data Optical distortion Distortion pattern recognition Image reconstruction

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Recursions Are All You Need: Towards Efficient Deep Unfolding Networks

Recursions Are All You Need: Towards Efficient Deep Unfoldin...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Rawwad Alhejaili Motaz Alfarraj Hamzah Luqman Ali Al-Shaikhi Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://***/Rawwad-Alhejaili/Recursions-Are-All-You-Need.

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Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

Seeing Through the Data: A Statistical Evaluation of Prohibi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Brian K. S. Isaac-Medina Seyma Yucer Neelanjan Bhowmik Toby P. Breckon Department of Computer Science Durham University UK Department of Engineering Durham University UK

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection performance standard (ECAC Common Testing Methodology for Automated Prohibited Item Detection Systems). However, the ever-increasing volume of detection work in this application area is highly reliant on a limited set of large-scale benchmark detection datasets that are specific to this domain. This study provides a comprehensive quantitative analysis of the underlying distribution of the prohibited item instances in three of the most prevalent X-ray security imagery benchmark and how these correlate against the detection performance of six state-of-the-art object detectors spanning multiple contemporary object detection paradigms. We focus on object size, location and aspect ratio within the image in addition to looking at global properties such as image colour distribution. Our results show a clear correlation between false negative (missed) detections and object size with the distribution of undetected items being statistically smaller in size than those typically found in the corresponding dataset as a whole. For false positive detections, the size distribution of such false alarm instances is shown to differ from the corresponding dataset test distribution in all cases. Furthermore, we observe that one-stage, anchor-free object detectors may be more vulnerable to the detection of heavily occluded or cluttered objects than other approaches whilst the detection of smaller prohibited item instances such as bullets remains more challenging than other object types.

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Certified Adversarial Robustness Within Multiple Perturbation Bounds

Certified Adversarial Robustness Within Multiple Perturbatio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Soumalya Nandi Sravanti Addepalli Harsh Rangwani R. Venkatesh Babu Vision and AI Lab Indian Institute of Science Bengaluru

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to ℓ 2 norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other ℓ p norm bounds as well. In general, a specific noise distribution is optimal for defending against a given ℓ p norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel certification scheme, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel training noise distribution along with a regularized training scheme to improve the certification within both ℓ 1 and ℓ 2 perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both ℓ 1 and ℓ 2 perturbation bounds. Code available at https://***/valiisc/NU-Certified-Robustness

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Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images 18

Visual interpretability analysis of Deep CNNs using an Adapt...

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18th ieee/CVF International conference on computer vision (ICCV)

作者： Ioannou, George Papagiannis, Tasos Tagaris, Thanos Alexandridis, Georgios Stafylopatis, Andreas Natl Tech Univ Athens Artificial Intelligence & Learning Syst Lab Sch Elect & Comp Engn Zografos 15780 Greece

ISBN: (纸本)9781665401913

Deep neural networks have been dominating the field of computer vision, achieving exceptional performance on object detection and pattern recognition. However, despite the highly accurate predictions of these models, the continuous increase in depth and complexity comes at the cost of interpretability, making the task of explaining the reasoning behind these predictions very challenging. In this paper, an analysis of state-of-the-art approaches towards the direction of interpreting the networks' representations, is carried out over two Diabetic Retinopathy image datasets, IDRiD and DDR. Furthermore, these techniques are compared in the task of image segmentation of the same datasets. This is to discover which method can produce the better attention maps that can solve the problem of segmentation without actually training the network for the specific task. To accomplish that we propose an adaptive threshold method that transforms the attention masks in a more suitable representation for segmentation. Experiments over multiple architectures were conducted to ensure the robustness of the results.

关键词： Training Image segmentation Visualization computer vision Retinopathy Computational modeling Transforms

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A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yangyi Liu Huan Liu Liangyan Li Zijun Wu Jun Chen McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023_ITBdehaze.

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Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion

Diversity is Definitely Needed: Improving Model-Agnostic Zer...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jordan Shipard Arnold Wiliem Kien Nguyen Thanh Wei Xiang Clinton Fookes Signal Processing Artificial Intelligence and Vision Technologies (SAIVT) Queensland University of Technology Australia Sentient Vision Systems Australia School of Computing Engineering and Mathematical Sciences La Trobe University Australia

In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our bag of tricks. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models. All code is available at https://***/Jordan-HS/Diversity_is_Definitely_Needed

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Sheng Yebin Liu Jingyi Yu Gaochang Wu Wei Xiong Ruixuan Cong Rongshan Chen Longzhao Guo Yanlin Xie Shuo Zhang Song Chang Youfang Lin Wentao Chao Xuechun Wang Guanghui Wang Fuqing Duan Tun Wang Da Yang Zhenglong Cui Sizhe Wang Mingyuan Zhao Qiong Wang Qianyu Chen Zhengyu Liang Yingqian Wang Jungang Yang Xueting Yang Junli Deng LFNAT 2023 Challenge State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University and Beihang Hangzhou Innovation Institute Yuhang

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research.

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