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Robustness Analysis on Foundational Segmentation Models

Robustness Analysis on Foundational Segmentation Models

引用

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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BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced P...

引用

2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technological University S-Lab Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a simi-lar computational constraint. In particular, our model Ba-sicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge. © 2022 ieee.

关键词： computer vision Computational modeling Superresolution Spatiotemporal phenomena pattern recognition Task analysis

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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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The New Agronomists: Language Models are Experts in Crop Management

The New Agronomists: Language Models are Experts in Crop Man...

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

作者： Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan University of Illinois at Urbana-Champaign Cornell University University of South Florida University of Alabama

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing simple neural-network-based reinforcement learning (RL) agents. Building on this foundation, this paper introduces a more advanced intelligent crop management system. This system uniquely combines RL, a language model (LM), and crop simulations facilitated by the Decision Support System for Agrotechnology Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model’s capacity to understand states and explore optimal management practices. The empirical results reveal that the LM exhibits superior learning capabilities. Through simulation experiments with maize crops in Florida (US) and Zaragoza (Spain), the LM not only achieves state-of-the-art performance under various evaluation metrics but also demonstrates a remarkable improvement of over 49% in economic profit, coupled with reduced environmental impact when compared to baseline methods. Our code is available at https://***/jingwu6/LM_AG.

关键词： Decision support systems Profitability Noise Green products Crops Deep reinforcement learning pattern recognition

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

引用

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

作者： Haoxiang Wang Pavan Kumar Anasosalu Vasu Fartash Faghri Raviteja Vemulapalli Mehrdad Farajtabar Sachin Mehta Mohammad Rastegari Oncel Tuzel Hadi Pouransari University of Illinois Urbana-Champaign Apple

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multitask learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： Training computer vision Visualization Computational modeling Semantic segmentation Semantics Merging

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Probing Conceptual Understanding of Large Visual-Language Models

Probing Conceptual Understanding of Large Visual-Language Mo...

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

作者： Madeline Schiappa Raiyaan Abdullah Shehreen Azad Jared Claypoole Michael Cogswell Ajay Divakaran Yogesh Rawat Center for Research in Computer Vision University of Central Florida SRI International

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual grasp of the visual content. In this work we focus on conceptual understanding of these large V+L models. To facilitate this study, we propose novel benchmarking datasets for probing three different aspects of content understanding, 1) relations, 2) composition, and 3) context. Our probes are grounded in cognitive science and help determine if a V+L model can, for example, determine if snow garnished with a man is implausible, or if it can identify beach furniture by knowing it is located on a beach. We experimented with many recent state-of-the-art V+L models and observe that these models mostly fail to demonstrate a conceptual understanding. This study reveals several interesting insights such as that cross-attention helps learning conceptual understanding, and that CNNs are better with texture and patterns, while Transformers are better at color and shape. We further utilize some of these insights and investigate a simple finetuning technique that rewards the three conceptual understanding measures with promising initial results. The proposed benchmarks will drive the community to delve deeper into conceptual understanding and foster advancements in the capabilities of large V+L models. The code and dataset is available at: https://***/vlm-robustness

关键词： Training Visualization Shape Snow Color Benchmark testing Transformers

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Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

Key Patches Are All You Need: A Multiple Instance Learning F...

引用

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

作者： D. J. Araújo M. R. Verdelho A. Bissoto J. C. Nascimento C. Santiago C. Barata LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal Institute of Computing Recod.ai Lab University of Campinas Brazil Lisbon ELLIS Unit

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often taking advantage of dataset bias to improve results for in-domain data, but jeopardizing their generalization capabilities. In this paper, we propose to limit the amount of information these models use to reach the final classification, by using a multiple instance learning (MIL) framework. MIL forces the model to use only a (small) subset of patches in the image, identifying discriminative regions. This mimics the clinical procedures, where medical decisions are based on localized findings. We evaluate our framework on two medical applications: skin cancer diagnosis using dermoscopy and breast cancer diagnosis using mammography. Our results show that using only a subset of the patches does not compromise diagnostic performance for in-domain data, compared to the baseline approaches. However, our approach is more robust to shifts in patient demographics, while also providing more detailed explanations about which regions contributed to the decision. Code is available at: https://***/diogojpa99/Medical-Multiple-Instance-Learning.

关键词： Image analysis Pipelines MIMICs Focusing Breast cancer pattern recognition Mirrors

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Exploring AI-Based Satellite Pose Estimation: from Novel Synthetic Dataset to In-Depth Performance Evaluation

Exploring AI-Based Satellite Pose Estimation: from Novel Syn...

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

作者： Fabien Gallet Christophe Marabotto Thomas Chambon Institut de Recherche Technologique Saint Exupéry

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision-based pose estimation using deep learning offers a promising cost effective and versatile solution for relative satellite navigation purposes. Using such a solution in closed loop to control spacecraft position is challenging from validation and performance verification viewpoint, because of the complex specification and development process. The validation task entails bridging the gap between the dataset and real-world data. In particular, modelling of Sun power and spectrum, Earth albedo, and atmospheric absence effects, is costly to replicate on ground. This article suggests a novel approach to produce synthetic space scene images. Fine statistical balancing is ensured to train and assess pose estimation solutions. A physically based camera model is used. Synthetic images incorporate realistic light flux, radiometric properties, and texture scatterings. The dataset comprises 120000 images supplemented with masks, distance maps, celestial body positions, and precise camera parameters (dataset publicly available https://***/space_rendezvous/ created in the frame of a project called RAPTOR: Robotic and Artificial intelligence Processing Test On Representative target). An analysis method using a dedicated metric library has been developed to help the assessment of the solution performance and robustness. A deeper comprehension of algorithm behavior through distribution law fitting and outlier identification is then facilitated. Finally, it is shown that implementing Region-of-Interest (RoI) training can drastically increase the performance of the Convolutional Neural Networks (CNNs) for long-range satellite pose estimation tasks.

关键词： Training Space vehicles computer vision Atmospheric modeling Pose estimation Satellite broadcasting Robot vision systems

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3D Clothed Human Reconstruction from Sparse Multi-View Images

3D Clothed Human Reconstruction from Sparse Multi-View Image...

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

作者： Jin Gyu Hong Seung Young Noh Hee Kyung Lee Won Sik Cheong Ju Yong Chang Dept of ECE Kwangwoon University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Clothed human reconstruction based on implicit functions has recently received considerable attention. In this study, we explore the most effective 2D feature fusion method from multi-view inputs experimentally and propose a method utilizing the 3D coarse volume predicted by the network to provide a better 3D prior. We fuse 2D features using an attention-based method to obtain detailed geometric predictions. In addition, we propose depth and color projection networks that predict the coarse depth volume and the coarse color volume from the input RGB images and depth maps, respectively. Coarse depth volume and coarse color volume are used as 3D priors to predict occupancy and texture, respectively. Further, we combine the fused 2D features and 3D features extracted from our 3D prior to predict occupancy and propose a technique to adjust the influence of 2D and 3D features using learnable weights. The effectiveness of our method is demonstrated through qualitative and quantitative comparisons with recent multi-view clothed human reconstruction models.

关键词： Solid modeling computer vision Three-dimensional displays Image color analysis Fuses conferences Feature extraction

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Event-Based Eye Tracking. AIS 2024 Challenge Survey

Event-Based Eye Tracking. AIS 2024 Challenge Survey

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

作者： Zuowen Wang Chang Gao Zongwei Wu Marcos V. Conde Radu Timofte Shih-Chii Liu Qinyu Chen Zheng-jun Zha Wei Zhai Han Han Bohao Liao Yuliang Wu Zengyu Wan Zhong Wang Yang Cao Ganchao Tan Jinze Chen Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Oliver Coenen Baoheng Zhang Yizhao Gao Jingyuan Li Hayden Kwok-Hay So Philippe Bich Chiara Boretti Luciano Prono Mircea Lică David Dinucu-Jianu Cătălin Grîu Xiaopeng Lin Hongwei Ren Bojun Cheng Xinan Zhang Valentin Vial Anthony Yezzi James Tsai Institute of Neuroinformatics University of Zurich and ETH Zurich Delft University of Technology University of Würzburg Leiden University University of Science and Technology of China Brainchip Inc The University of Hong Kong Polytechnic of Turin The Hong Kong University of Science and Technology (Guangzhou) Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

关键词： Surveys computer vision Visualization Reviews Gaze tracking Cameras Transformers

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