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共 11891 条记录，以下是1041-1050 订阅

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Leveraging per Image-Token Consistency for vision-Language Pre-training

Leveraging per Image-Token Consistency for Vision-Language P...

引用

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

作者： Gou, Yunhao Ko, Tom Yang, Hansi Kwok, James Zhang, Yu Wang, Mingxuan Southern Univ Sci & Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China ByteDance Ai Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://***/gyhdog99/epic

关键词： Multi-modal learning

来源：评论

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The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector

The Treasure Beneath Multiple Annotations: An Uncertainty-aw...

引用

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

作者： Zhou, Caixia Huang, Yaping Pu, Mengyang Guan, Qingji Huang, Li Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY USA

ISBN: (纸本)9798350301298

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://***/ZhouCX117/UAED.

关键词： Low-level vision

来源：评论

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A Light Touch Approach to Teaching Transformers Multi-view Geometry

A Light Touch Approach to Teaching Transformers Multi-view G...

引用

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

作者： Bhalgat, Yash Henriques, Joao F. Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350301298

Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid laws). To resolve this conundrum, we propose a "light touch" approach, guiding visual Transformers to learn multiple-view geometry but allowing them to break free when needed. We achieve this by using epipolar lines to guide the Transformer's cross-attention maps during training, penalizing attention values outside the epipolar lines and encouraging higher attention along these lines since they contain geometrically plausible matches. Unlike previous methods, our proposal does not require any camera pose information at test-time. We focus on pose-invariant object instance retrieval, where standard Transformer networks struggle, due to the large differences in viewpoint between query and retrieved images. Experimentally, our method outperforms state-of-the-art approaches at object retrieval, without needing pose information at test-time.

关键词： detection recognition: Categorization retrieval

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Learning Federated Visual Prompt in Null Space for MRI Reconstruction

Learning Federated Visual Prompt in Null Space for MRI Recon...

引用

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

作者： Feng, Chun-Mei Li, Bangjun Xu, Xinxing Liu, Yong Fu, Huazhu Zuo, Wangmeng ASTAR Inst High Performance Comp WPC Singapore Singapore Shangdong Univ Sch Informat Sci & Engn Jinan Peoples R China Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy. However, the data heterogeneity caused by different MRI protocols, insufficient local training data, and limited communication bandwidth inevitably impair global model convergence and updating. In this paper, we propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction. FedPR is a new federated paradigm that adopts a powerful pre-trained model while only learning and communicating the prompts with few learnable parameters, thereby significantly reducing communication costs and achieving competitive performance on limited local data. Moreover, to deal with catastrophic forgetting caused by data heterogeneity, FedPR also updates efficient federated visual prompts that project the local prompts into an approximate null space of the global prompt, thereby suppressing the interference of gradients on the server performance. Extensive experiments on federated MRI show that FedPR significantly outperforms state-of-the-art FL algorithms with < 6% of communication costs when given the limited amount of local training data.

关键词： cell microscopy Medical and biological vision

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On the Importance of Accurate Geometry Data for Dense 3D vision Tasks

On the Importance of Accurate Geometry Data for Dense 3D Vis...

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

作者： Jung, HyunJun Ruhkamp, Patrick Zhai, Guangyao Brasch, Nikolas Li, Yitong Verdie, Yannick Song, Jifei Zhou, Yiren Armagan, Anil Ilic, Slobodan Leonardis, Ales Navab, Nassir Busam, Benjamin Tech Univ Munich Munich Germany Dwe Ai Munich Germany Huawei Noahs Ark Lab Montreal PQ Canada Siemens AG Munich Germany

ISBN: (纸本)9798350301298

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset1 comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion.

关键词： 3D from single images

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DIP: Dual Incongruity Perceiving Network for Sarcasm Detection

DIP: Dual Incongruity Perceiving Network for Sarcasm Detecti...

引用

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

作者： Wen, Changsong Jia, Guoli Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image and text as demonstrated in psychological theories. To tackle this issue, we propose a Dual Incongruity Perceiving (DIP) network consisting of two branches to mine the sarcastic information from factual and affective levels. For the factual aspect, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings, and leverage gaussian distribution to model the uncertain correlation caused by the incongruity. The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data. For the affective aspect, we utilize siamese layers with shared parameters to learn cross-modal sentiment information. Furthermore, we use the polarity value to construct a relation graph for the mini-batch, which forms the continuous contrastive loss to acquire affective embeddings. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches. Our code is released on https://***/downdric/MSD.

关键词： vision applications and systems

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Fake it till you make it: Learning transferable representations from synthetic ImageNet clones

Fake it till you make it: Learning transferable representati...

引用

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

作者： Sariyildiz, Mert Bulent Alahari, Karteek Larlus, Diane Kalantidis, Yannis NAVER LABS Europe Grenoble France Uni Grenoble Alpes CNRS Inria Grenoble INPLJK Grenoble France

ISBN: (纸本)9798350301298

Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://***/imagenet-sd

关键词： detection recognition: Categorization retrieval

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BiasAdv: Bias-Adversarial Augmentation for Model Debiasing

BiasAdv: Bias-Adversarial Augmentation for Model Debiasing

引用

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

作者： Lim, Jongin Kim, Youngdong Kim, Byungjai Ahn, Chanho Shin, Jinwoo Yang, Eunho Han, Seungju Samsung Adv Inst Technol SAIT Seoul South Korea Korea Adv Inst Sci & Technol KAIST Seoul South Korea

ISBN: (纸本)9798350301298

Neural networks are often prone to bias toward spurious correlations inherent in a dataset, thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the significant lack of bias-conflicting training data (i.e., samples without spurious correlations). In this paper, we propose a novel data augmentation approach termed Bias-Adversarial augmentation (BiasAdv) that supplements bias-conflicting samples with adversarial images. Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model. Specifically, we formulate an optimization problem for generating adversarial images that attack the predictions of an auxiliary biased model without ruining the predictions of the desired debiased model. Despite its simplicity, we find that BiasAdv can generate surprisingly useful synthetic bias-conflicting samples, allowing the debiased model to learn generalizable representations. Furthermore, BiasAdv does not require any bias annotations or prior knowledge of the bias type, which enables its broad applicability to existing debiasing methods to improve their performances. Our extensive experimental results demonstrate the superiority of BiasAdv, achieving state-of-the-art performance on four popular benchmark datasets across various bias domains.

关键词： detection recognition: Categorization retrieval

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itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

itKD: Interchange Transfer-based Knowledge Distillation for ...

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

作者： Cho, Hyeon Choi, Junyong Baek, Geonwoo Hwang, Wonjun Ajou Univ Suwon South Korea Hyundai Motor Co Seoul South Korea Naver AI Lab Seongnam South Korea

ISBN: (纸本)9798350301298

Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprising channel-wise compression and decompression via interchange transfer-based knowledge distillation. To learn the map-view feature of a teacher network, the features from teacher and student networks are independently passed through the shared autoencoder;here, we use a compressed representation loss that binds the channel-wised compression knowledge from both student and teacher networks as a kind of regularization. The decompressed features are transferred in opposite directions to reduce the gap in the interchange reconstructions. Lastly, we present an head attention loss to match the 3D object detection information drawn by the multi-head self-attention mechanism. Through extensive experiments, we verify that our method can train the lightweight model that is well-aligned with the 3D point cloud detection task and we demonstrate its superiority using the well-known public datasets;e.g., Waymo and nuScenes.(1)

关键词： Efficient and scalable vision

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GENIE: Show Me the Data for Quantization

GENIE: Show Me the Data for Quantization

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

作者： Jeon, Yongkweon Lee, Chungman Kim, Ho-young Samsung Res Berkeley Hts NJ 07922 USA

ISBN: (纸本)9798350301298

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters (mu and sigma) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called GENIE that generates data suited for quantization. With the data synthesized by GENIE, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach. The code is available at https://***/SamsungLabs/Genie.

关键词： Efficient and scalable vision

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