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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是31-40 订阅

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How you feelin'? Learning Emotions and Mental States in Movie Scenes

How you feelin'? Learning Emotions and Mental States in Movi...

引用

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

作者： Srivastava, Dhruv Singh, Aditya Kumar Tapaswi, Makarand IIIT Hyderabad CVIT Hyderabad Telangana India

ISBN: (纸本)9798350301298

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

关键词： language reasoning vision

来源：评论

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Interactive and Explainable Region-guided Radiology Report Generation

Interactive and Explainable Region-guided Radiology Report G...

引用

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

作者： Tanida, Tim Muller, Philip Kaissis, Georgios Rueckert, Daniel Tech Univ Munich Munich Germany Helmholtz Zentrum Munich Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://***/ttanida/rgrg.

关键词： cell microscopy Medical and biological vision

来源：评论

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Inverse Rendering of Translucent Objects using Physical and Neural Renderers

Inverse Rendering of Translucent Objects using Physical and ...

引用

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

作者： Li, Chenhao Trung Thanh Ngo Nagahara, Hajime Osaka Univ Inst Databil Sci Suita Osaka Japan

ISBN: (纸本)9798350301298

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model. Code and Data are available at https://***/ligoudaner377/homo_translucent

关键词： vision + graphics

来源：评论

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Deep Semi-supervised Metric Learning with Mixed Label Propagation

Deep Semi-supervised Metric Learning with Mixed Label Propag...

引用

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

作者： Zhuang, Furen Moulin, Pierre Univ Illinois Dept ECE Urbana IL 61801 USA ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore

ISBN: (纸本)9798350301298

Metric learning requires the identification of far-apart similar pairs and close dissimilar pairs during training, and this is difficult to achieve with unlabeled data because pairs are typically assumed to be similar if they are close. We present a novel metric learning method which circumvents this issue by identifying hard negative pairs as those which obtain dissimilar labels via label propagation (LP), when the edge linking the pair of data is removed in the affinity matrix. In so doing, the negative pairs can be identified despite their proximity, and we are able to utilize this information to significantly improve LP's ability to identify far-apart positive pairs and close negative pairs. This results in a considerable improvement in semi-supervised metric learning performance as evidenced by recall, precision and Normalized Mutual Information (NMI) performance metrics on Content-based Information Retrieval (CBIR) applications.

关键词： detection recognition: Categorization retrieval

来源：评论

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Connecting vision and Language with Video Localized Narratives

Connecting Vision and Language with Video Localized Narrativ...

引用

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

作者： Voigtlaender, Paul Changpinyo, Soravit Pont-Tuset, Jordi Soricut, Radu Ferrari, Vittorio Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives [36], annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://***/video-localized-narratives/.

关键词： Datasets and evaluation

来源：评论

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3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

3D Highlighter: Localizing Regions on 3D Shapes via Text Des...

引用

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

作者： Decatur, Dale Lang, Itai Hanocka, Rana Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350301298

We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https: //***/threedle/3DHighlighter.

关键词： vision + graphics

来源：评论

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Decentralized Learning with Multi-Headed Distillation

Decentralized Learning with Multi-Headed Distillation

引用

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

作者： Zhmogiov, Andrey Sandler, Mark Miller, Nolan Kristiansen, Gus Vladymyrov, Max Google AI 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to share their data, weights or weight updates. Our approach is communication efficient, utilizes an unlabeled public dataset and uses multiple auxiliary heads for each client, greatly improving training efficiency in the case of heterogeneous data. This approach allows individual models to preserve and enhance performance on their private tasks while also dramatically improving their performance on the global aggregated data distribution. We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation.

关键词： Efficient and scalable vision

来源：评论

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Robust Unsupervised StyleGAN Image Restoration

Robust Unsupervised StyleGAN Image Restoration

引用

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

作者： Poirier-Ginter, Yohan Lalonde, Jean-Franois Univ Cote Dazur INRIA Nice France Univ Laval Quebec City PQ Canada

ISBN: (纸本)9798350301298

GAN-based image restoration inverts the generative process to repair images corrupted by known degradations. Existing unsupervised methods must be carefully tuned for each task and degradation level. In this work, we make StyleGAN image restoration robust: a single set of hyperparameters works across a wide range of degradation levels. This makes it possible to handle combinations of several degradations, without the need to retune. Our proposed approach relies on a 3-phase progressive latent space extension and a conservative optimizer, which avoids the need for any additional regularization terms. Extensive experiments demonstrate robustness on inpainting, upsampling, denoising, and deartifacting at varying degradations levels, outperforming other StyleGAN-based inversion techniques. Our approach also favorably compares to diffusion-based restoration by yielding much more realistic inversion results. Code is available at the above URL.

关键词： Low-level vision

来源：评论

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Model-Agnostic Gender Debiased Image Captioning

Model-Agnostic Gender Debiased Image Captioning

引用

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

作者： Hirota, Yusuke Nakashima, Yuta Garcia, Noa Osaka Univ Suita Osaka 565 Japan

ISBN: (纸本)9798350301298

Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models. While prior work has addressed this problem by forcing models to focus on people to reduce gender misclassification, it conversely generates gender-stereotypical words at the expense of predicting the correct gender. From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender. To mitigate both types of gender biases, we propose a framework, called LIBRA, that learns from synthetically biased samples to decrease both types of biases, correcting gender misclassification and changing gender-stereotypical words to more neutral ones.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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Improving Image recognition by Retrieving from Web-Scale Image-Text Data

Improving Image Recognition by Retrieving from Web-Scale Ima...

引用

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

作者： Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.

关键词： detection recognition: Categorization retrieval

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