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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22907 条记录，以下是4651-4660 订阅

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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

引用

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

作者： Maria Parelli Alexandros Delitzas Nikolas Hars Georgios Vlassis Sotirios Anagnostidis Gregor Bachmann Thomas Hofmann ETH Zurich Switzerland

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model’s 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features.

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Learning by Aligning Videos in Time

Learning by Aligning Videos in Time

引用

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

作者： Haresh, Sanjay Kumar, Sateesh Coskun, Huseyin Syed, Shahram N. Konin, Andrey Zia, M. Zeeshan Quoc-Huy Tran Retrocausal Inc Seattle WA 98052 USA

ISBN: (纸本)9781665445092

We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking.

关键词： Training computer vision Costs Performance gain pattern recognition Task analysis

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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

LLM-Seg: Bridging Image Segmentation and Large Language Mode...

引用

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

作者： Junchi Wang Lei Ke ETH Zurich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://***/wangjunchi/LLMSeg.

关键词： Training Image segmentation Large language models Design methodology Pipelines Cognition pattern recognition

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PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection

PUDD: Towards Robust Multi-modal Prototype-based Deepfake De...

引用

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

作者： Alvaro Lopez Pellcier Yi Li Plamen Angelov School of Computing and Communications Lancaster University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learning-based deepfake detection methods, particularly with diffusion models, have shown remarkable progress. However, there is a growing demand for real-world applications to detect unseen individuals, deepfake techniques, and scenarios. To address this limitation, we propose a Prototype-based Unified Framework for Deepfake Detection (PUDD). PUDD offers a detection system based on similarity, comparing input data against known prototypes for video classification and identifying potential deepfakes or previously unseen classes by analyzing drops in similarity. Our extensive experiments reveal three key findings: (1) PUDD achieves an accuracy of 95.1% on Celeb-DF, outperforming state-of-the-art deepfake detection methods; (2) PUDD leverages image classification as the upstream task during training, demonstrating promising performance in both image classification and deepfake detection tasks during inference; (3) PUDD requires only 2.7 seconds for retraining on new data and emits 10 5 times less carbon compared to the state-of-the-art model, making it significantly more environmentally friendly.

关键词： Training Deepfakes computer vision conferences Computational modeling Prototypes Diffusion models

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Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

Cross-Domain Gradient Discrepancy Minimization for Unsupervi...

引用

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

作者： Du, Zhekai Li, Jingjing Su, Hongzu Zhu, Lei Lu, Ke Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Shandong Normal Univ Jinan Shandong Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (bi-classifier) has been introduced into UDA which is effective to align distributions between different domains. Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers. However, the similarity of the outputs cannot guarantee the accuracy of target samples, i.e., traget samples may match to wrong categories even if the discrepancy between two classifiers is small. To challenge this issue, in this paper, we propose a cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples. Specifically, the gradient gives a cue for the semantic information of target samples so it can be used as a good supervision to improve the accuracy of target samples. In order to compute the gradient signal of target smaples, we further obtain target pseudo labels through a clustering-based self-supervised learning. Extensive experiments on three widely used UDA datasets show that our method surpasses many previous state-of-the-arts.

关键词： computer vision Semantics Minimization Adversarial machine learning pattern recognition Reliability

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SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes

SemiGPC: Distribution-Aware Label Refinement for Imbalanced ...

引用

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

作者： Abdelhak Lemkhenter Manchen Wang Luca Zancato Gurumurthy Swaminathan Paolo Favaro Davide Modolo AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as Co-Match [17] and SimMatch [34], our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity. This explicit control allows SemiGPC to be more robust to confirmation bias especially under class imbalance. We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch [23], ReMixMatch [4], SimMatch [34] and FreeMatch [32] and different pre-training strategies including MSN [2] and Dino [5]. We also show that SemiGPC achieves state of the art results under different degrees of class imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime. Using SemiGPC also results in about 2% avg. accuracy increase compared to a new competitive baseline on the more challenging benchmarks SemiAves, SemiCUB, SemiFungi [27] and Semi-iNat [26].

关键词： computer vision Sensitivity Accuracy conferences Gaussian processes Semisupervised learning Predictive models

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Multiresolution Knowledge Distillation for Anomaly Detection

Multiresolution Knowledge Distillation for Anomaly Detection

引用

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

作者： Salehi, Mohammadreza Sadjadi, Niousha Baselizadeh, Soroosh Rohban, Mohammad H. Rabiee, Hamid R. Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781665445092

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the "distillation" of features at various layers of an expert network, which is pre-trained on ImageNet, into a simpler cloner network to tackle both issues. We detect and localize anomalies using the discrepancy between the expert and cloner networks' intermediate activation values given an input sample. We show that considering multiple intermediate hints in distillation leads to better exploitation of the expert's knowledge and a more distinctive discrepancy between the two networks, compared to utilizing only the last layer activation values. Notably, previous methods either fail in precise anomaly localization or need expensive region-based training. In contrast, with no need for any special or intensive training procedure, we incorporate interpretability algorithms in our novel framework to localize anomalous regions. Despite the striking difference between some test datasets and ImageNet, we achieve competitive or significantly superior results compared to SOTA on MNIST, F-MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two other medical datasets on both anomaly detection and localization.

关键词： Training Location awareness Knowledge engineering computer vision Image resolution pattern recognition Task analysis

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

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

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

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Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

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

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

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Scaling Local Self-Attention for Parameter Efficient Visual Backbones

Scaling Local Self-Attention for Parameter Efficient Visual ...

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

作者： Vaswani, Ashish Ramachandran, Prajit Srinivas, Aravind Parmar, Niki Hechtman, Blake Shlens, Jonathon Google Res Mountain View CA 94043 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutions.(1)

关键词： computer vision Visualization Image segmentation Computational modeling Transfer learning Memory management Object detection

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