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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

引用

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

作者： Sreetama Sarkar Souvik Kundu Kai Zheng Peter A. Beerel Universiy of Southern California Los Angeles USA Intel Labs San Diego USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4× and compute cost by up to 2× while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： Training computer vision Costs Quantization (signal) Codes Computational modeling conferences

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Two Stage Dehazing Framework for Dense and Non-Homogeneous Dehazing

Two Stage Dehazing Framework for Dense and Non-Homogeneous D...

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

作者： Wei Song Yichang Gao Jiahao Xiong Hualiang Lin Dong Li Yun Zhang Guangdong University of Technology Guangzhou China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world environments, haze often causes a decrease in visibility, leading to potentially severe consequences. Although current methods based on the assumption of homogeneous haze density have achieved commendable results, dehazing techniques for non-homogeneous density haze still fall short in terms of visibility restoration and color accuracy. We observe that although single-stage methods have made significant strides, a multi-stage enhancement can further improve dehazing in terms of both visibility and color restoration. In this paper, we propose a two-stage dehaze framework, named Two Stage Dehazing Framework. Our approach consists of a DehazeNet, which does not require specifying a particular model for dehazing and can accept a hazy image as input, producing an clear image of the same dimensions as the original. Two such DehazeNet are sequentially connected to form the final serial DehazeNet. Moreover, to better approximate the output image to real-world scenes, we propose the Multi-Scale Attention Head. Our method achieved third place in NTIRE 2024 Dense and NonHomogeneous Dehazing Challenge, demonstrating outstanding performance metrics in the Peak Signal to Noise Ratio (PSNR), the Structral Similarity Index (SSIM), and the Mean Opinion Score (MOS). Related code will be acailable on code.

关键词： Training Measurement computer vision PSNR Head Codes Image color analysis

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AAPL: Adding Attributes to Prompt Learning for vision-Language Models

AAPL: Adding Attributes to Prompt Learning for Vision-Langua...

引用

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

作者： Gahyeon Kim Sohee Kim Seokju Lee Korea Institute of Energy Technology (KENTECH)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zeroshot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from high-level class information when inducing bias in learnable prompts. Through our novel mechanism called "Adding Attributes to Prompt Learning", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.

关键词： Visualization Zero-shot learning Semantics Focusing Feature extraction Data augmentation Vectors

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PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

PointPrompt: A Multi-modal Prompting Dataset for Segment Any...

引用

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

作者： Jorge Quesada Mohammad Alotaibi Mohit Prabhushankar Ghassan AlRegib OLIVES Lab Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The capabilities of foundation models, most recently the Segment Anything Model, have gathered a large degree of attention for providing a versatile framework for tackling a wide array of image segmentation tasks. However, the interplay between human prompting strategies and the segmentation performance of these models remains understudied, as does the role played by the domain knowledge that humans (by previous exposure) and models (by pretraining) bring to the prompting process. To bridge this gap, we present the PointPrompt dataset compiled across multiple image modalities as well as multiple prompting annotators per modality. We collected a total of 16 image datasets from the natural, underwater, medical and seismic domain in order to create a comprehensive resource to facilitate the study of prompting behavior and agreement across modalities. Overall, our prompting dataset contains 158880 inclusion points and 52594 exclusion points over a total of 6000 images. Our analysis highlights the following: (i) viability of prompts across heterogeneous data, (ii) that point prompts are a valuable resource in the effort for enhancing the robustness and generalizability of segmentation models across diverse domains, (iii) prompts facilitate an understanding of the dynamics between annotation strategies and neural network outcomes. Information on downloading the dataset, images, and prompting tool is provided on our project website https://***/pointprompt/.

关键词： Bridges Image segmentation computer vision Computational modeling conferences Neural networks Dynamic scheduling

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Thermal Image Super-Resolution Challenge - PBVS 2021

Thermal Image Super-Resolution Challenge - PBVS 2021

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Nathan, Sabari Kansal, Priya Mehri, Armin Ardakani, Parichehr Behjati Dalal, Anurag Akula, Aparna Sharma, Darshika Pandey, Shashwat Kumar, Basant Yao, Jiaxin Wu, Rongyuan Feng, Kai Li, Ning Zhao, Yongqiang Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Sarvaiya, Anjali Upla, Kishor P. Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Almasri, Feras Vandamme, Thomas Debeir, Olivier Gutierrez, Nolan B. Nguyen, Quan H. Beksi, William J. Escuela Super Politecn Litoral ESPOL Fac Ingn Elect & Comp CIDIS Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Couger Inc Tokyo Japan Cent Sci Instruments Org Chandigarh India Indian Inst Engn Sci & Technol Sibpur India MNNIT Allahabad Prayagraj India Northwestern Polytech Univ Xian Peoples R China SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665448994

This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used;only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image;this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.

关键词： Training Measurement computer vision conferences Superresolution Cameras pattern recognition

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DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera

DemosaicFormer: Coarse-to-Fine Demosaicing Network for Hybri...

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

作者： Senyan Xu Zhijing Sun Jiaying Zhu Yurui Zhu Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hybrid Event-Based vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors, offering substantial benefits for applications requiring low-light, high dynamic range, and low-latency environments, such as smartphones and wearable devices. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. To address this challenge, in this study, we propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction. Coarse demosaicing network is designed to produce a preliminary high-quality estimate of the RGB image from the HybridEVS raw data while the pixel correction network enhances the performance of image restoration and mitigates the impact of defective pixels. Our key innovation is the design of a Multi-Scale Gating Module (MSGM) applying the integration of cross-scale features, which allows feature information to flow between different scales. Additionally, the adoption of progressive training and data augmentation strategies further improves model’s robustness and effectiveness. Experimental results show superior performance against the existing methods both qualitatively and visually, and our DemosaicFormer achieves the best performance in terms of all the evaluation metrics in the MIPI 2024 challenge on Demosaic for Hybridevs Camera. The code is available at this repository.

关键词： Training Measurement Technological innovation vision sensors Cameras Data augmentation Robustness

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Latent Flow Diffusion for Deepfake Video Generation

Latent Flow Diffusion for Deepfake Video Generation

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

作者： Aashish Chandra K Aashutosh A V Srijan Das Abhijit Das Birla Institute of Technology & Science Pilani India University of North Carolina Charlotte USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image-to-video generation with conditional identity swap popularly known as deepfake, aims to synthesize a new video for the target identity guided by an image of the target and a video of the source identity. The biggest challenge of these tasks lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given target image and source video. In this paper, we propose a deepfake generation technique using novel latent flow diffusion (LFD) that includes an optical flow sequence in the latent space based on a given source video to warp the given target image. Compared to previous works on video diffusion, our proposed LFD can swap the spatial details maintaining temporal information by utilizing the spatial content of the given target image and employing the latent flow of the source video. Our model consists of three stages: a Flow predictor model captures the optical flow of the source video, two-fold Transformer encoding layers predict the driving frame and a conditioned image-to-video generator guided by the driving frame generates the final deep fake video. We conducted multiple experiments and our proposed model has consistently outperformed prior video diffusion models for deepfake generation.

关键词： Deepfakes computer vision Image motion analysis Image coding Predictive models Transformers Generators

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Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models

Exploring Real World Map Change Generalization of Prior-Info...

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

作者： Samuel M. Bateman Ning Xu H. Charles Zhao Yael Ben Shalom Vince Gong Greg Long Will Maddern Nuro Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe, driverless deployments remains a significant challenge. Recent work on these models proposes training robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors. In this paper, we investigate how models trained on these synthetically perturbed map priors generalize to performance on deployment-scale, real world map changes. We present a large-scale experimental study to determine which synthetic perturbations are most useful in generalizing to real world HD map changes, evaluated using multiple years of real-world autonomous driving data. We show there is still a substantial sim2real gap between synthetic prior perturbations and observed real-world changes, which limits the utility of current prior-informed HD map prediction models.

关键词： Training computer vision Perturbation methods conferences Noise Buildings Predictive models

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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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MultiPanoWise: holistic deep architecture for multi-task dense prediction from a single panoramic image

MultiPanoWise: holistic deep architecture for multi-task den...

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

作者： Uzair Shah Muhammad Tukur Mahmood Alzubaidi Giovanni Pintore Enrico Gobbetti Mowafa Househ Jens Schneider Marco Agus ICT Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Visual and Data-Intensive Computing CRS4 Italy National Research Center in HPC Big Data and Quantum Computing Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a novel holistic deep-learning approach for multi-task learning from a single indoor panoramic image. Our framework, named MultiPanoWise, extends vision transformers to jointly infer multiple pixel-wise signals, such as depth, normals, and semantic segmentation, as well as signals from intrinsic decomposition, such as reflectance and shading. Our solution leverages a specific architecture combining a transformer-based encoder-decoder with multiple heads, by introducing, in particular, a novel context adjustment approach, to enforce knowledge distillation between the various signals. Moreover, at training time we introduce a hybrid loss scalarization method based on an augmented Chebychev/hypervolume scheme. We illustrate the capabilities of the proposed architecture on public-domain synthetic and real-world datasets. We demonstrate performance improvements with respect to the most recent methods specifically designed for single tasks, like, for example, individual depth estimation or semantic segmentation. To our knowledge, this is the first architecture capable of achieving state-of-the-art performance on the joint extraction of heterogeneous signals from single indoor omnidirectional images.

关键词： Training Reflectivity computer vision Semantic segmentation Estimation computer architecture Multitasking

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