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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30983 条记录，以下是4611-4620 订阅

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The High Variety of Image Wafer Inspection Field 14

The High Variety of Image Wafer Inspection Field

引用

14th International conference on Machine vision (ICMV)

作者： Barone, Massimiliano Fedeli, Matteo STMicroelectronics Milan Italy

ISBN: (数字)9781510650459

ISBN: (纸本)9781510650459;9781510650442

This paper contains three different problems and solutions for detecting defects in very large-scale integration of wafer images. The goal is to explain the typical categories of wafer inspection problems: topology, device inspection, wafer analysis. Unusual and particular cases have been selected to show the high problem variety into the same wafer inspection field. This aspect implies solutions very different and specific variations of classic image analysis techniques and computer vision methods.

关键词： Topology device test wafer test image wafer inspection template matching image registration pattern recognition VLSI wafer images golden template segmented comparison space mapping

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Cross-Stage Class-Specific Attention for Image Semantic Segmentation 5th

Cross-Stage Class-Specific Attention for Image Semantic Segm...

引用

5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Shi, Zhengyi Sun, Li Li, Qingli Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China East China Normal Univ Key Lab Adv Theory & Applicat Stat & Data Sci Shanghai Peoples R China

ISBN: (纸本)9783031189159;9783031189166

Recent backbones built on transformers capture the context within a significantly larger area than CNN, and greatly improve the performance on semantic segmentation. However, the fact, that the decoder utilizes features from different stages in the shallow layers, indicates that local context is still important. Instead of simply incorporating features from different stages, we propose a cross-stage class-specific attention mainly for transformer-based backbones. Specifically, given a coarse prediction, we first employ the final stage features to aggregate a class center within the whole image. Then high-resolution features from the earlier stage are used as queries to absorb the semantics from class centers. To eliminate the irrelevant classes within a local area, we build the context for each query position according to the classification score from coarse prediction, and remove the redundant classes. So only relevant classes provide keys and values in attention and participate the value routing. We validate the proposed scheme on different datasets including ADE20K, Pascal Context and COCO-Stuff, showing that the proposed model improves the performance compared with other works.

关键词： Semantic segmentation vision transformer Attention algorithm

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Enhancing Targeted Attack Transferability via Diversified Weight Pruning

Enhancing Targeted Attack Transferability via Diversified We...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hung-Jui Wang Yu-Yu Wu Shang-Tse Chen National Taiwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Malicious attackers generate adversarial instances by introducing imperceptible perturbations into data. Even in the black-box setting where model details are concealed, attackers still exploit networks with cross-model transferability. Despite the notable success of untargeted attacks, achieving targeted attack transferability remains a challenging endeavor. Recent investigations have demonstrated the efficacy of ensemble-based techniques. However, utilizing additional models to carry out ensemble attacks brings extra costs. To reduce the number of white-box models required, model augmentation methods augment the given network to produce a variant of diverse models, contributing useful gradients for attack. In this work, we propose Diversified Weight Pruning (DWP) as an innovative model augmentation technique specifically designed to facilitate the generation of transferable targeted attacks. In contrast to prior techniques, DWP preserves essential connections while simultaneously ensuring diversity among the pruned models, both of which are identified as pivotal factors for targeted transferability. DWP is shown effective with experiments on ImageNet under challenging conditions, with enhancements of up to 10.1%, 6.6%, and 7.0% across adversarially trained models, Non-CNN architectures, and Google Cloud vision respectively.

关键词： computer vision Image coding Costs Perturbation methods conferences computer architecture pattern recognition

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Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA: A Cause-Effect Look at Language Bias

引用

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

作者： Niu, Yulei Tang, Kaihua Zhang, Hanwang Lu, Zhiwu Hua, Xian-Sheng Wen, Ji-Rong Nanyang Technol Univ Singapore Singapore Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data.

关键词： computer vision Codes Linguistics Robustness Cognition pattern recognition

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WonderJourney: Going from Anywhere to Everywhere

WonderJourney: Going from Anywhere to Everywhere

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hong-Xing Yu Haoyi Duan Junhwa Hur Kyle Sargent Michael Rubinstein William T. Freeman Forrester Cole Deqing Sun Noah Snavely Jiajun Wu Charles Herrmann Stanford University Google Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce WonderJourney, a modular framework for perpetual 3D scene generation. Unlike prior work on view generation that focuses on a single type of scenes, we start at any user-provided location (by a text description or an image), and generate a journey through a long sequence of diverse yet coherently connected 3D scenes. We leverage an LLM to generate textual descriptions of the scenes in this journey, a text-driven point cloud generation pipeline to make a compelling and coherent sequence of 3D scenes, and a large VLM to verify the generated scenes. We show compelling, diverse visual results across various scene types and styles, forming imaginary “wonder journeys ”. Project website: https://***/WonderJourney/. “No, no! The adventures first, explanations take such a dreadful time.“ - Alice's Adventures in Wonderland

关键词： Point cloud compression Visualization computer vision Three-dimensional displays Pipelines pattern recognition

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Defending Multimodal Fusion Models against Single-Source Adversaries

Defending Multimodal Fusion Models against Single-Source Adv...

引用

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

作者： Yang, Karren Lin, Wan-Yi Barman, Manash Condessa, Filipe Kolter, Zico MIT Cambridge MA 02139 USA Bosch Ctr AI Renningen Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities. In this paper, we investigate the robustness of multimodal neural networks against worst-case (i.e., adversarial) perturbations on a single modality. We first show that standard multimodal fusion models are vulnerable to single-source adversaries: an attack on any single modality can overcome the correct information from multiple unperturbed modalities and cause the model to fail. This surprising vulnerability holds across diverse multimodal tasks and necessitates a solution. Motivated by this finding, we propose an adversarially robust fusion strategy that trains the model to compare information coming from all the input sources, detect inconsistencies in the perturbed modality compared to the other modalities, and only allow information from the unperturbed modalities to pass through. Our approach significantly improves on state-of-the-art methods in single-source robustness, achieving gains of 7.8-25.2% on action recognition, 19.7-48.2% on object detection, and 1.6-6.7% on sentiment analysis, without degrading performance on unperturbed (i.e., clean) data.

关键词： Training Sentiment analysis computer vision Perturbation methods Neural networks Object detection Robustness

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Temporal Action Segmentation from Timestamp Supervision

Temporal Action Segmentation from Timestamp Supervision

引用

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

作者： Li, Zhe Abu Farha, Yazan Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781665445092

Temporal action segmentation approaches have been very successful recently. However;annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse than filly supervised approaches. In this paper, we propose to use timestamp supervision for the temporal action segmentation task. Timestamps require a comparable annotation effort to weakly supervised approaches, and yet provide a more supervisory signal. To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations. Our approach uses the model output and the annotated timestamps to generate frame-wise labels by detecting the action changes. We further introduce a confidence loss that forces the predicted probabilities to monotonically decrease as the distance to the timestamps increases. This ensures that all and not only the most distinctive frames of an action are learned during training. The evaluation on four datasets shows that models trained with timestamps annotations achieve comparable performance to the fully supervised approaches.

关键词： Training computer vision Annotations Computational modeling Predictive models pattern recognition Task analysis

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Predicting Human Scanpaths in Visual Question Answering

Predicting Human Scanpaths in Visual Question Answering

引用

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

作者： Chen, Xianyu Jiang, Ming Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665445092

Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art.

关键词： Training Visualization computer vision Reinforcement learning Predictive models Tools Knowledge discovery

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Visual Semantic Role Labeling for Video Understanding

Visual Semantic Role Labeling for Video Understanding

引用

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

作者： Sadhu, Arka Gupta, Tanmay Yatskar, Mark Nevatia, Ram Kembhavi, Aniruddha Univ Southern Calif Los Angeles CA 90007 USA Univ Penn Philadelphia PA 19104 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9781665445092

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging task of semantic role labeling in videos or VidSRL, we introduce the VidSitu benchmark, a large scale video understanding data source with 29K 10-second movie clips richly annotated with a verb and semantic-roles every 2 seconds. Entities are co-referenced across events within a movie clip and events are connected to each other via event-event relations. Clips in VidSitu are drawn from a large collection of movies (similar to 3K) and have been chosen to be both complex (similar to 4.2 unique verbs within a video) as well as diverse (similar to 200 verbs have more than 100 annotations each). We provide a comprehensive analysis of the dataset in comparison to other publicly available video understanding benchmarks, several illustrative baselines and evaluate a range of standard video recognition models. Our code and dataset is available at ***.

关键词： Visualization computer vision Annotations Semantics Benchmark testing Motion pictures pattern recognition

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Strategies to Leverage Foundational Model Knowledge in Object Affordance Grounding

Strategies to Leverage Foundational Model Knowledge in Objec...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Arushi Rai Kyle Buettner Adriana Kovashka Department of Computer Science University of Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

An important task for intelligent systems is affordance grounding, where the goal is to locate regions on an object where an action can be performed. Past weakly supervised approaches learn from human-object interaction (HOI) by transferring grounding knowledge from exocentric to ego-centric views of an object. The use of HOI priors is inherently noisy and thus provides a limited source of supervision. To address this challenge, we identify that recent foundational models (i.e. VLMs and LLMs) can serve as auxiliary sources of knowledge for frameworks due to their vast world knowledge. In this work, we propose strategies to extract and leverage foundational model knowledge related to attributes and object parts to enhance an HOI-based affordance grounding framework. In particular, we propose to combine HOI and foundational model priors through (1) a spatial consistency loss and (2) heatmap aggregation. Our strategies result in mKLD and mNSS improvements, and insights suggest future directions for improving affordance grounding capabilities.

关键词： Heating systems computer vision Grounding Affordances conferences Pipelines pattern recognition

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