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TAME: Task Agnostic Continual Learning using Multiple Experts

TAME: Task Agnostic Continual Learning using Multiple Expert...

引用

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

作者： Haoran Zhu Maryam Majzoubi Arihant Jain Anna Choromanska New York University Google

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic, setting where the task identities are not known and the learning machine needs to infer them from the observations. Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner. At training, the strategy for switching between tasks hinges on an extremely simple observation that for each new coming task there occurs a statistically-significant deviation in the value of the loss function that marks the onset of this new task. At inference, the switching between experts is governed by the selector network that forwards the test sample to its relevant expert network. The selector network is trained on a small subset of data drawn uniformly at random. We control the growth of the task expert networks as well as selector network by employing pruning. Our experimental results show the efficacy of our approach on benchmark continual learning data sets, outperforming the previous task-agnostic methods and even the techniques that admit task identities at both training and testing, while at the same time using a comparable model size.

关键词： Training Continuing education computer vision conferences Computational modeling Switches Fasteners

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Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation

Diagnostic Benchmark and Iterative Inpainting for Layout-Gui...

引用

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

作者： Jaemin Cho Linjie Li Zhengyuan Yang Zhe Gan Lijuan Wang Mohit Bansal UNC Chapel Hill Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine- grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits.

关键词： Training computer vision Image synthesis Shape conferences Layout Benchmark testing

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Robustness Analysis on Foundational Segmentation Models

Robustness Analysis on Foundational Segmentation Models

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

作者： Madeline Chantry Schiappa Shehreen Azad Sachidanand Vs Yunhao Ge Ondrej Miksik Yogesh S Rawat Vibhav Vineet Center for Research in Computer Vision University of Central Florida Indian Institute of Technology Madras University of Southern California Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of multi-modal data using self-supervised or semi-supervised learning have emerged. These "foundation" models are often adapted to a variety of downstream tasks like classification, object detection, and segmentation with little-to-no training on the target dataset. In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks and focus on robustness against real-world distribution shift inspired perturbations. We benchmark seven state-of-the-art segmentation architectures using 2 different perturbed datasets, MS COCO-P and ADE20K-P, with 17 different perturbations with 5 severity levels each. Our findings reveal several key insights: (1) VFMs exhibit vulnerabilities to compression-induced corruptions, (2) despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and (3) VFMs demonstrate enhanced robustness for certain object categories. These observations suggest that our robustness evaluation framework sets new requirements for foundational models, encouraging further advancements to bolster their adaptability and performance. The code and dataset is available at: https://***/fm-robust.

关键词： Training Adaptation models Visualization Analytical models Perturbation methods Computational modeling Benchmark testing

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NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Learning the Super-Resolution Space Challenge

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Busch, Christoph Chen, Yang Cheng, Jian Chudasama, Vishal Gang, Ruipeng Gao, Shangqi Gao, Kun Gong, Laiyun Han, Qingrui Huang, Chao Jin, Zhi Jo, Younghyun Kim, Seon Joo Kim, Younggeun Lee, Seungjun Lei, Yuchen Li, Chu-Tak Li, Chenghua Li, Ke Liu, Zhi-Song Liu, Youming Nan, Nan Park, Seung-Ho Patel, Heena Peng, Shichong Prajapati, Kalpesh Qi, Haoran Raja, Kiran Ramachandra, Raghavendra Siu, Wan-Chi Son, Donghee Song, Ruixia Upla, Kishor Wang, Li-Wen Wang, Yatian Wang, Junwei Wu, Qianyu Xu, Xinhua Yang, Sejong Yuan, Zhen Zhang, Liting Zhang, Huanrong Zhang, Junkai Zhang, Yifan Zhang, Zhenzhou Zhou, Hangqi Zhu, Aichun Zhuang, Xiahai Zou, Jiaxin Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Zhejiang Univ Hangzhou Peoples R China NetEaseYunXin Beijing Peoples R China Yonsei Univ Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Ulsan South Korea Lomin Inc Portland OR USA Fudan Univ Shanghai Peoples R China Simon Fraser Univ Burnaby BC Canada Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China North China Univ Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China CASIA Beijing Peoples R China NRTA Beijing Peoples R China NCUT Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Southeast Univ Lab Image Sci & Technol Nanjing Peoples R China SVNIT Surat Gujarat India NTNU Trondheim Norway

ISBN: (纸本)9781665448994

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

关键词： Measurement computer vision conferences Superresolution Predictive models pattern recognition

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

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

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

引用

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

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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NeRF as Pretraining at Scale: Generalizable 3D-Aware Semantic Representation Learning from View Prediction

NeRF as Pretraining at Scale: Generalizable 3D-Aware Semanti...

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

作者： Wenyan Cong Hanxue Liang Zhiwen Fan Peihao Wang Yifan Jiang Dejia Xu A. Cengiz Oztireli Zhangyang Wang University of Texas at Austin University of Cambridge

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cross-scene generalizable NeRF models, which could directly synthesize novel views using several source views of unseen scenes, are gaining prominence in the NeRF field. Discovering the potential signal of emerging capabilities in existing methods, we draw a parallel between BERT’s "drop-and-predict" Masked Language Model (MLM) pre-training and novel view synthesis (NVS) in generalizable NeRF. In this work, we pioneer the scaling up of NVS as an effective pretraining strategy in a multi-view context. To bolster generalizability in pretraining, we incorporate a large-scale, minimally annotated dataset and proportionally increase the model size, revealing a neural scaling law akin to that observed in BERT. We also introduce innovative hardness-aware training techniques to enhance robust feature learning. Our model, named "NPS", demonstrates remarkable generalizability in both zero-shot and few-shot novel view synthesis. It further shows emergent capabilities in downstream tasks like few-shot multi-view semantic segmentation and depth estimation. Significantly, NPS reduces the necessity of training separate models for each task, underlining its versatility and efficiency. This approach sets a new precedent in the NeRF field, and highlights the vast possibilities opened up by scaling up generalizable novel view synthesis.

关键词： Training Representation learning computer vision Semantic segmentation conferences Semantics Estimation

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Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

Feature Corrective Transfer Learning: End-to-End Solutions t...

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

作者： Chuheng Wei Guoyuan Wu Matthew J. Barth University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in the field of object detection lies in the system’s performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces ‘Feature Corrective Transfer Learning’, a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model’s ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

关键词： Visualization computer vision Accuracy Computational modeling Surveillance Transfer learning Imaging

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Towards Real-Time 4K Image Super-Resolution

Towards Real-Time 4K Image Super-Resolution

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

作者： Eduard Zamfir Marcos V. Conde Radu Timofte Computer Vision Lab CAIDAS IFI University of Würzburg Germany

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard. While higher resolutions offer improved visual quality for users, they pose a significant challenge for super-resolution networks to achieve real-time performance on commercial GPUs. This paper presents a comprehensive analysis of super-resolution model designs and techniques aimed at efficiently upscaling images from 720p and 1080p resolutions to 4K. We begin with a simple, effective baseline architecture and gradually modify its design by focusing on extracting important high-frequency details efficiently. This allows us to subsequently downscale the resolution of deep feature maps, reducing the overall computational footprint, while maintaining high reconstruction fidelity. We enhance our method by incorporating pixel-unshuffling, a simplified and speed-up reinterpretation of the basic block proposed by NAFNet, along with structural re-parameterization. We assess the performance of the fastest version of our method in the new NTIRE 2023 Real-Time 4K Super-Resolution challenge and demonstrate its potential in comparison with state-of-the-art efficient super-resolution models when scaled up. Our method was tested successfully on high-quality content from photography, digital art, and gaming content.

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