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共 20950 条记录，以下是661-670 订阅

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A Comprehensive Analysis of Factors Impacting Membership Inference

A Comprehensive Analysis of Factors Impacting Membership Inf...

引用

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

作者： DeAlcala, Daniel Mancera, Gonzalo Morales, Aythami Fierrez, Julian Tolosana, Ruben Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain

ISBN: (纸本)9798350365474

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Face recognition Fairness Membership Inference MIA MINT Realiability

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TOPLight: Lightweight Neural Networks with Task-Oriented Pretraining for Visible-Infrared recognition

TOPLight: Lightweight Neural Networks with Task-Oriented Pre...

引用

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

作者： Yu, Hao Cheng, Xu Peng, Wei Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing Peoples R China Stanford Univ Dept Psychiat & Behav Sci Stanford CA USA

ISBN: (纸本)9798350301298

Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such methods ignore the negative influence of the pretrained colour prior knowledge, as well as their heavy computational burden makes them hard to deploy in actual scenarios with limited resources. In this paper, we propose a novel task-oriented pretrained lightweight neural network (TOPLight) for VI recognition. Specifically, the TOPLight method simulates the domain conflict and sample variations with the proposed fake domain loss in the pretraining stage, which guides the network to learn how to handle those difficulties, such that a more general modality-shared feature representation is learned for the heterogeneous images. Moreover, an effective fine-grained dependency reconstruction module (FDR) is developed to discover substantial pattern dependencies shared in two modalities. Extensive experiments on VI person reidentification and VI face recognition datasets demonstrate the superiority of the proposed TOPLight, which significantly outperforms the current state of the arts while demanding fewer computational resources.

关键词： body gesture Humans: Face movement pose

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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...

引用

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

作者： Tang, Yujin Dong, Peijie Tang, Zhenheng Chu, Xiaowen Liang, Junwei Hong Kong Univ Sci & Technol Guangzhou AI Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Guangzhou DSA Thrust Guangzhou Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge;CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://***/yyyujintang/VMRNN-PyTorch.

关键词： Spatiotemporal Forecasting State Space Model Video Prediction

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LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

LAVENDER: Unifying Video-Language Understanding as Masked La...

引用

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

作者： Li, Linjie Can, Zhe Lin, Kevin Lin, Chung-Ching Liu, Zicheng Liu, Ce Wang, Lijuan Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9798350301298

Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and training objectives for each task. In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling [13] (MLM) is used as the common interface for all pre-training and downstream tasks. Such unification leads to a simplified model architecture, where only a lightweight MLM head, instead of a decoder with much more parameters, is needed on top of the multimodal encoder. Surprisingly, experimental results show that this unified framework achieves competitive performance on 14 VidL benchmarks, covering video question answering, text-to-video retrieval and video captioning. Extensive analyses further demonstrate LAVENDER can (i) seamlessly support all downstream tasks with just a single set of parameter values when multi-task fine-tuned;(ii) generalize to various downstream tasks with limited training samples;and (iii) enable zero-shot evaluation on video question answering tasks. Code is available at https://***/microsoft/LAVENDER.

关键词： and reasoning language vision

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De-noised vision-language Fusion Guided by Visual Cues for E-commerce Product Search

De-noised Vision-language Fusion Guided by Visual Cues for E...

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

作者： Hu, Zhizhang Li, Shasha Du, Ming Dhua, Arnab Gray, Douglas Univ Calif Merced Merced CA 95343 USA Amazon Visual Search & AR Seattle WA USA Amazon Seattle WA USA

ISBN: (纸本)9798350365474

In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this misalignment can stem from noisy and redundant non-visual-descriptive text attributes in the product description. To address this, we introduce the MultiModal alignment-guided Learned Token Pruning (MM-LTP) method. MM-LTP employs token pruning, conventionally used for computational efficiency, to perform online text cleaning during multimodal model training. By enabling the model to discern and discard unimportant tokens, it is able to train with implicitly cleaned image-text pairs. We evaluate MM-LTP using a benchmark multimodal e-commerce dataset comprising over 710,000 unique Amazon products. Our evaluation hinges on visual search, a prevalent e-commerce feature. Through MM-LTP, we demonstrate that refining text tokens enhances the paired image branch's training, which leads to significantly improved visual search performance.

关键词： De-noised Fusion Multimodal Learning Token Pruning vision-language Model Visual Search

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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for vision Decoding

Seeing Beyond the Brain: Conditional Diffusion Model with Sp...

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

作者： Chen, Zijiao Qing, Jiaxin Xiang, Tiange Yue, Wan Lin Zhou, Juan Helen Natl Univ Singapore Singapore Singapore Chinese Univ Hong Kong Hong Kong Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350301298

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively;the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

关键词： cell microscopy Medical and biological vision

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Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking

Constrained Evolutionary Diffusion Filter for Monocular Endo...

引用

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

作者： Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China

ISBN: (纸本)9798350301298

Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm.

关键词： cell microscopy Medical and biological vision

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DisWOT: Student Architecture Search for Distillation WithOut Training

DisWOT: Student Architecture Search for Distillation WithOut...

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

作者： Dong, Peijie Li, Lujun Wei, Zimian Natl Univ Def Technol Changsha Peoples R China Chinese Acad Sci Beijing 100864 Peoples R China

ISBN: (纸本)9798350301298

Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a novel training-free framework to search for the best student architectures for a given teacher. Our work first empirically show that the optimal model under vanilla training cannot be the winner in distillation. Secondly, we find that the similarity of feature semantics and sample relations between random-initialized teacher-student networks have good correlations with final distillation performances. Thus, we efficiently measure similarity matrixs conditioned on the semantic activation maps to select the optimal student via an evolutionary algorithm without any training. In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180x training acceleration. Additionally, we extend similarity metrics in DisWOT as new distillers and KD-based zero-proxies. Our experiments on CIFAR, ImageNet and NAS-Bench-201 demonstrate that our technique achieves state-of-the-art results on different search spaces. Our project and code are available at https://***/DisWOT-cvpr2023/.

关键词： Efficient and scalable vision

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Learning Debiased Representations via Conditional Attribute Interpolation

Learning Debiased Representations via Conditional Attribute ...

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

作者： Zhang, Yi-Kai Wang, Qi-Wei Zhan, De-Chuan Ye, Han-Jia Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

An image is usually described by more than one attribute like "shape" and "color". When a dataset is biased, i.e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn. To improve the generalization ability when training on such a biased dataset, we propose a chi(2)-model to learn debiased representations. First, we design a.-shape pattern to match the training dynamics of a DNN and find Intermediate Attribute Samples (IASs) samples near the attribute decision boundaries, which indicate how the value of an attribute changes from one extreme to another. Then we rectify the representation with a chi-structured metric learning objective. Conditional interpolation among IASs eliminates the negative effect of peripheral attributes and facilitates retaining the intra-class compactness. Experiments show that chi(2)-model learns debiased representation effectively and achieves remarkable improvements on various datasets. Code is available at: https: //***/ZhangYikaii/chi-square

关键词： accountability ethics in vision fairness privacy Transparency

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Learning to Detect and Segment for Open Vocabulary Object Detection

Learning to Detect and Segment for Open Vocabulary Object De...

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

作者： Wang, Tao Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9798350301298

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

关键词： detection recognition: Categorization retrieval

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