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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11889 条记录，以下是21-30 订阅

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Learning to Count without Annotations

Learning to Count without Annotations

引用

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

作者： Knobel, Lukas Han, Tengda Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands Univ Oxford Oxford England

ISBN: (纸本)9798350353006

While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR, a model that can learn this task without requiring any manual annotations. To this end, we construct "Self-Collages", images with various pasted objects as training samples, that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple base-lines and generic models such as FasterRCNN and DETR, but also matches the performance of supervised counting models in some domains.

关键词： computer vision self-supervision visual counting

来源：评论

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FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong vision-Language models

FFF: Fixing Flawed Foundations in contrastive pre-training r...

引用

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

作者： Bulat, Adrian Ouali, Yassine Tzimiropoulos, Georgios Samsung AI Ctr Cambridge Cambridge England Tech Univ Iasi Iasi Romania Queen Mary Univ London London England

ISBN: (纸本)9798350353006

Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically, we firstly study and analyze two issues affecting training: incorrect assignment of negative pairs, and low caption quality and diversity. Then, we devise effective solutions for addressing both problems, which essentially require training with multiple true positive pairs. Finally, we propose training with sigmoid loss to address such a requirement. We show very large gains over the current state-of-the-art for both image recognition (similar to +6% on average over 11 datasets) and image retrieval (similar to +19% on Flickr30k and similar to +15% on MSCOCO).

关键词： Visual languages

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LAFS: Landmark-based Facial Self-supervised Learning for Face recognition

LAFS: Landmark-based Facial Self-supervised Learning for Fac...

引用

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

作者： Sun, Zhonglin Feng, Chen Patras, Ioannis Tzimiropoulos, Georgios Queen Mary Univ London London England

ISBN: (纸本)9798350353013;9798350353006

In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover, motivated by one recent finding, that is, the face saliency area is critical for face recognition, in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining, we utilize patches localized by extracted facial landmarks. This enables our method - namely LAndmark-based Facial Self-supervised learning (LAFS), to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations, we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks, especially on more challenging few-shot scenarios. The code is available at https://***/szlbiubiubiulLAFS_cvpr2024.

关键词： Face recognition Facial Parts Few-shot Self-supervised

来源：评论

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Technical Report of NICE Challenge at cvpr 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores

Technical Report of NICE Challenge at CVPR 2024: Caption Re-...

引用

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

作者： Jeong, Kiyoon Lee, Woojun Nam, Woongchan Ma, Minjeong Kang, Pilsung Korea Univ Sch Ind & Management Engn Seoul South Korea

ISBN: (纸本)9798350365474

This report presents the ECO (Ensembled Clip score and cOnsensus score) pipeline from team DSBA LAB, which is a new framework used to evaluate and rank captions for a given image. ECO selects the most accurate caption describing image. It is made possible by combining an Ensembled CLIP score, which considers the semantic alignment between the image and captions, with a Consensus score that accounts for the essentialness of the captions. Using this framework, we achieved notable success in the cvpr 2024 Workshop Challenge on Caption Re-ranking Evaluation at the New Frontiers for Zero-Shot Image Captioning Evaluation (NICE). Specifically, we secured third place based on the CIDEr metric, second in both the SPICE and METEOR metrics, and first in the ROUGE-L and all BLEU Score metrics. The code and configuration for the ECO framework are available at https://***/DSBA-Lab/ECO

关键词： Caption Evaluation Caption Reranking Image Captioning

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Training vision Transformers for Semi-Supervised Semantic Segmentation

Training Vision Transformers for Semi-Supervised Semantic Se...

引用

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

作者： Hu, Xinting Jiang, Li Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Munich Germany

ISBN: (纸本)9798350353013;9798350353006

We present S(4)Former, a novel approach to training vision Transformers for Semi-Supervised Semantic Segmentation (S-4). At its core, S(4)Former employs a vision Transformer within a classic teacher-student framework, and then leverages three novel technical ingredients: PatchShujjle as a parameter-free perturbation technique, Patch-Adaptive Self-Attention (PASA) as a fine-grained feature modulation method, and the innovative Negative Class Ranking (NCR) regularization loss. Based on these regularization modules aligned with Transformer-specific characteristics across the image input, feature, and output dimensions, S(4)Former exploits the Transformer's ability to capture and difef rentiate consistent global contextual information in unlabeled images. Overall, S(4)Former not only defines a new state of the art in s(4) but also maintains a streamlined and scalable architecture. Being readily compatible with existingframeworks, S(4)Former achieves strong improvements (up to 4.9%) on benchmarks like Pascal VOC 2012, COCO, and Cityscapes, with varying numbers of labeled data. The code is at https://***/JoyHuYY1412/S4Former.

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One-Shot Open Affordance Learning with Foundation Models

One-Shot Open Affordance Learning with Foundation Models

引用

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

作者： Li, Gen Sun, Deqing Sevilla-Lara, Laura Jampani, Varun Univ Edinburgh Edinburgh Midlothian Scotland Google Res Mountain View CA USA Stabil AI London England

ISBN: (纸本)9798350353013;9798350353006

We introduce One-shot Open Affordance Learning (OOAL), where a model is trained with just one example per base object category, but is expected to identify novel objects and affordances. While vision-language models excel at recognizing novel objects and scenes, they often struggle to understand finer levels of granularity such as affordances. To handle this issue, we conduct a comprehensive analysis of existing foundation models, to explore their inherent understanding of affordances and assess the potential for data-limited affordance learning. We then propose a vision-language framework with simple and effective designs that boost the alignment between visual features and affordance text embeddings. Experiments on two affordance segmentation benchmarks show that the proposed method outperforms state-of-the-art models with less than 1% of the full training data, and exhibits reasonable generalization capability on unseen objects and affordances. Project page: https://***/ooal.

关键词： Affordance Foundation Models vision-Language Models Visual Affordance Learning

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Global Latent Neural Rendering

Global Latent Neural Rendering

引用

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

作者： Tanay, Thomas Maggioni, Matteo Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

A recent trend among generalizable novel view synthesis methods is to learn a rendering operator acting over single camera rays. This approach is promising because it removes the need for explicit volumetric rendering, but it effectively treats target images as collections of independent pixels. Here, we propose to learn a global rendering operator acting over all camera rays jointly. We show that the right representation to enable such rendering is a 5-dimensional plane sweep volume consisting of the projection of the input images on a set of planes facing the target camera. Based on this understanding, we introduce our Convolutional Global Latent Renderer (ConvGLR), an efficient convolutional architecture that performs the rendering operation globally in a low-resolution latent space. Experiments on various datasets under sparse and generalizable setups show that our approach consistently outperforms existing methods by significant margins.

关键词： Training computer vision Computational modeling computer architecture Rendering (computer graphics) Cameras Market research

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Frozen Feature Augmentation for Few-Shot Image Classification

Frozen Feature Augmentation for Few-Shot Image Classificatio...

引用

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

作者： Bar, Andreas Houlsby, Neil Dehghani, Mostafa Kumar, Manoj Google DeepMind London England Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9798350353006

Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper, we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space, dubbed 'frozen feature augmentation (FroFA)', covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA, such as brightness, can improve few-shot performance consistently across three network architectures, three large pre-training datasets, and eight transfer datasets.

关键词： data augmentation feature augmentation few-shot learning frozen features image classification large-scale pretraining transfer learning vision transformer

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PEEKABOO: Interactive Video Generation via Masked-Diffusion

PEEKABOO: Interactive Video Generation via Masked-Diffusion

引用

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

作者： Jain, Yash Nasery, Anshul Vineet, Vibhav Behl, Harkirat Microsoft Redmond WA 98052 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350353013;9798350353006

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present PEEKABOO, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that PEEKABOO achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.

关键词： computer vision diffusion interactive text to video video generation

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Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action recognition

Selective, Interpretable and Motion Consistent Privacy Attri...

引用

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

作者： Ilic, Filip Zhao, He Pock, Thomas Wildes, Richard P. Graz Univ Technol Graz Austria York Univ York N Yorkshire England

ISBN: (纸本)9798350353006

Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions, but also contextual regions important for action recognition. Lack of interpretability erodes trust in these new technologies. We highlight the limitations of current paradigms and propose a solution: Human selected privacy templates that yield interpretability by design, an obfuscation scheme that selectively hides attributes and also induces temporal consistency, which is important in action recognition. Our approach is architecture agnostic and directly modifies input imagery, while existing approaches generally require architecture training. Our approach offers more flexibility, as no training is required, and outperforms alternatives on three widely used datasets.

关键词： Action recognition Privacy Preservation Video Understanding

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