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ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

引用

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

作者： Ning-Chi Huang Chi-Chih Chang Wei-Cheng Lin Endri Taka Diana Marculescu Kai-Chiang Wu National Yang Ming Chiao Tung University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of *** this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9× reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ELSA.

关键词： Training Degradation computer vision Accuracy Transformers Throughput Software

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Two Stage Dehazing Framework for Dense and Non-Homogeneous Dehazing

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

引用

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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DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detec...

引用

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

作者： Johan Edstedt Georg Bökman Zhenjun Zhao Linköping University Chalmers University of Technology The Chinese University of Hong Kong Texas A&M University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we analyze and improve into the recently proposed DeDoDe keypoint detector. We focus our analysis on some key issues. First, we find that DeDoDe keypoints tend to cluster together, which we fix by performing nonmax suppression on the target distribution of the detector during training. Second, we address issues related to data augmentation. In particular, the DeDoDe detector is sensitive to large rotations. We fix this by including 90-degree rotations as well as horizontal flips. Finally, the decoupled nature of the DeDoDe detector makes evaluation of downstream usefulness problematic. We fix this by matching the keypoints with a pretrained dense matcher (RoMa) and evaluating two-view pose estimates. We find that the original long training is detrimental to performance, and therefore propose a much shorter training schedule. We integrate all these improvements into our proposed detector DeDoDe v2 and evaluate it with the original DeDoDe descriptor on the MegaDepth-1500 and IMC2022 benchmarks. Our proposed detector significantly increases pose estimation results, notably from 75.9 to 78.3 mAA on the IMC2022 challenge. Code and weights are available at ***/Parskatt/DeDoDe.

关键词： Training Schedules computer vision Codes conferences Pose estimation Detectors

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

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

引用

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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Semantic Pre-supplement for Exposure Correction

Semantic Pre-supplement for Exposure Correction

引用

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

作者： Zhen Zou Wei Yu Jie Huang Feng Zhao University of Science and Technology of China He Fei China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exposure correction tasks are dedicated to recovering the brightness and structural information of overexposed or underexposed images. The recovery difficulty of areas with different exposure levels is different, as severely exposed areas are more difficult to recover due to severe structural information loss than commonly exposed areas. However, existing methods focus on the simultaneous recovery of global brightness and structure, ignoring that the recovery difficulty varies between areas. To address this issue, we propose a novel exposure correction strategy named "Inpainting Assisted Exposure Correction"(IAEC), which pre-performs image structure repair on severely exposed areas to guide the exposure correction process. This method is based on the observation that the contextual semantic information contained in the image structure can effectively help the overall image recovery, and the lack of contextual semantic information in severely incorrectly exposed areas is very severe. The pre-performed structural repair by the inpainting model can well supplement the insufficient contextual semantic information caused by severe exposure. Therefore, we use an inpainting model to perform pre-structure repair on severely exposed areas to obtain supplementary contextual semantic information and then align the structure-repaired image with the improperly exposed input at the feature level. Extensive experiments demonstrate that our method gets superior results than the state-of-the-art methods and has the potential to be applied to other tasks with similar context loss problems.

关键词： Training computer vision conferences Semantics Brightness Maintenance engineering Cognition

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Image-caption difficulty for efficient weakly-supervised object detection from in-the-wild data

Image-caption difficulty for efficient weakly-supervised obj...

引用

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

作者： Giacomo Nebbia Adriana Kovashka University of Pittsburgh

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, we have witnessed the collection of larger and larger multi-modal, image-caption datasets: from hundreds of thousands such pairs to hundreds of millions. Such datasets allow researchers to build powerful deep learning models, at the cost of requiring intensive computational resources. In this work, we ask: can we use such datasets efficiently without sacrificing performance? We tackle this problem by extracting difficulty scores from each image-caption sample, and by using such scores to make training more effective and efficient. We compare two ways to use difficulty scores to influence training: filtering a representative subset of each dataset and ordering samples through curriculum learning. We analyze and compare difficulty scores extracted from a single modality—captions (i.e., caption length and number of object mentions) or images (i.e., region proposals’ size and number)—or based on alignment of image-caption pairs (i.e., CLIP and concreteness). We focus on Weakly-Supervised Object Detection where image-level labels are extracted from captions. We discover that (1) combining filtering and curriculum learning can achieve large gains in performance, but not all methods are stable across experimental settings, (2) singlemodality scores often outperform alignment-based ones, (3) alignment scores show the largest gains when training time is limited.

关键词： Training Deep learning computer vision Costs Filtering conferences Computational modeling

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Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution

Multi-Level Feature Fusion Network for Lightweight Stereo Im...

引用

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

作者： Yunxiang Li Wenbin Zou Qiaomu Wei Feng Huang Jing Wu Fuzhou University South China University of Technology Chengdu University of Information Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modules and cross-view feature interaction modules to make use of the information from stereo images is the focus of numerous methods. However, this adds a great deal of network parameters and structural redundancy. To facilitate the application of stereo image super-resolution in downstream tasks, we propose an efficient Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically, MFFSSR utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract multi-level intra-view features. Using the channel separation strategy, HAFEB can efficiently interact with the embedded cross-view interaction module. This structural configuration can efficiently mine features inside the view while improving the efficiency of cross-view information sharing. Hence, reconstruct image details and textures more accurately. Abundant experiments demonstrate the effectiveness of MFFSSR. We achieve superior performance with fewer parameters. The source code is available at https://***/KarosLYX/MFFSSR.

关键词： computer vision Source coding conferences Computational modeling Superresolution Redundancy Information sharing

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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...

引用

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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Must Unsupervised Continual Learning Relies on Previous Information?

Must Unsupervised Continual Learning Relies on Previous Info...

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

作者： Haoyang Cheng Haitao Wen Heqian Qiu Lanxiao Wang Minjian Zhang Hongliang Li University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open-world recognition has recently gained significant attention owing to its ability to bridge the gap between experimental scenarios and real-world applications. Since continual learning can learn from a sequence of dynamic data streams, it obtains extensive applications in open-world recognition. However, because of the production of data annotation is usually time-consuming and labor-intensive in real-world scenarios, it’s necessary to develop unsupervised continual learning. Recent studies start to investigate unsupervised continual learning (i.e., UCL), but mainly focus on rehearsal and regularization strategies to enhance the anti-forgetting capability of UCL. In practice, rehearsal and regularization are information-dependent, which require information from previous data as supervised signals, e.g., replayed data and previous model. In this paper, we propose an information-free method, Alternate Task Discrimination (ATD), which is a self-supervised pretext task for continuity and improves anti-forgetting capability via encouraging the model to discriminate which data stream current sample is from. The whole process doesn’t rely on any previous information. In order to perform ATD effectively in UCL framework, we design an alternating optimization algorithm where UCL and ATD are optimized respectively. We validate the effectiveness of the proposed method on multiple standard UCL benchmarks, where it obtains considerable improvements compared with baseline methods. In addition, our approach can be used as a plug-in unit, which makes further achievements when collaborated with existing popular UCL methods.

关键词： Continuing education computer vision conferences Production Benchmark testing Data models pattern recognition

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PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics

PitcherNet: Powering the Moneyball Evolution in Baseball Vid...

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

作者： Jerrin Bright Bavesh Balaji Yuhao Chen David A Clausi John S Zelek University of Waterloo Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the high-stakes world of baseball, every nuance of a pitcher’s mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game.

关键词： Solid modeling Three-dimensional displays Tracking Visual analytics Kinematics Games pattern recognition

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