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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022"

共 11141 条记录，以下是31-40 订阅

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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

来源：评论

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KeyPoint Relative Position Encoding for Face recognition

KeyPoint Relative Position Encoding for Face Recognition

引用

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

作者： Kim, Minchul Su, Yiyang Liu, Feng Jain, Anil Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350353013;9798350353006

In this paper, we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face recognition when image alignment failures occur. We propose a novel method called KP-RPE, which leverages key points (e.g. facial landmarks) to make ViT more resilient to scale, translation, and pose variations. We begin with the observation that Relative Position Encoding (RPE) is a good way to bring affine transform generalization to ViTs. RPE, however, can only inject the model with prior knowledge that nearby pixels are more important than far pixels. Keypoint RPE (KP-RPE) is an extension of this principle, where the significance of pixels is not solely dictated by their proximity but also by their relative positions to specific keypoints within the image. By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations. We show the merit of KP-RPE in face and gait recognition. The experimental results demonstrate the effectiveness in improving face recognition performance from low-quality images, particularly where alignment is prone to failure. Code and pre-trained models are available.

关键词： Affine Transformation Face recognition Facial Landmarks Gait recognition Keypoints recognition Relative Position Encoding

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OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video recognition

OST: Refining Text Knowledge with Optimal Spatio-Temporal De...

引用

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

作者： Chen, Tongjia Yu, Hongshan Yang, Zhengeng Li, Zechuan Sun, Wei Chen, Chen Hunan Univ Changsha Peoples R China Hunan Normal Univ Changsha Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL USA

ISBN: (纸本)9798350353006

Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names, leading to less distinct semantic space and potential performance limitations. In this work, we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names, we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover, to assign the best descriptors with different video instances, we propose Optimal Descriptor Solver, forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot, few-shot, and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.

关键词： Action recognition Multi-modality Video Understanding Video Reognition Video Understanding

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Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval

Do You Remember? Dense Video Captioning with Cross-Modal Mem...

引用

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

作者： Kim, Minkuk Kim, Hyeon Bae Moon, Jinyoung Choi, Jinwoo Kim, Seong Tae Kyung Hee Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9798350353006

There has been significant attention to the research on dense video captioning, which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However, addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study, we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features, the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model with-out extensive pretraining from a large video dataset. Our code is available at https://***/ailab-kyunghee/CM2_DVC.

关键词： Dense Video Captioning Launguage Reasoning Retrieval Agumented Generation Video Captioning vision

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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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From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with vision-Language Models

From Pixels to Graphs: Open-Vocabulary Scene Graph Generatio...

引用

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

作者： Li, Rongjie Zhang, Songyang Lin, Dahua Chen, Kai He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for down-stream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with novel visual relation concepts. To address this challenge, we introduce a new open-vocabulary SGG framework based on sequence generation. Our framework leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm. Specifically, we generate scene graph sequences via image-to-text generation with VLM and then construct scene graphs from these sequences. By doing so, we harness the strong capabilities of VLM for open-vocabulary SGG and seamlessly integrate explicit relational modeling for enhancing the VL tasks. Experimental results demonstrate that our design not only achieves superior performance with an open vocabulary but also enhances downstream vision-language task performance through explicit relation modeling knowledge.

关键词： Scene Graph Generation Scene Understanding vision-language

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Edit One for All: Interactive Batch Image Editing

Edit One for All: Interactive Batch Image Editing

引用

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

作者： Thao Nguyen Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae Univ Wisconsin Madison Madison WI 53707 USA

ISBN: (纸本)9798350353013;9798350353006

In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways;from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner. However, most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process, this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g., make the face frontal), our method can automatically transfer that edit to other test images, so that regardless of their initial state (pose), they all arrive at the same final state (e.g., all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods, while having more visual consistency and saving significant time and human effort.

关键词： batch image editing computer vision image editing image generation image manipulation

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ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

ParamISP: Learned Forward and Inverse ISPs using Camera Para...

引用

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

作者： Kim, Woohyeok Kim, Geonu Lee, Junyong Lee, Seungyong Baek, Seung-Hwan Cho, Sunghyun POSTECH Pohang South Korea Samsung AI Ctr Toronto Toronto ON Canada Samsung Toronto ON Canada

ISBN: (纸本)9798350353006

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time, and have limitations when used for various applications. In this paper, we propose ParamISP, a learning-based method for forward and inverse conversion between sRGB and RAW images, that adopts a novel neural-network module to utilize camera parameters, which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data, ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAWand sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and camera-to-camera transfer.

关键词： Low-level vision

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Flexible Biometrics recognition: Bridging the Multimodality Gap through Attention, Alignment and Prompt Tuning

Flexible Biometrics Recognition: Bridging the Multimodality ...

引用

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

作者： Tiong, Leslie Ching Ow Sigmund, Dick Chan, Chen-Hui Teoh, Andrew Beng Jin Samsung Elect Suwon South Korea AIDOT Inc Seoul South Korea Korea Inst Sci & Technol Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we introduce Flexible Biometric recognition (FBR), a novel framework designed to advance conventional face, periocular, and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the vision Transformer architecture. MFA facilitates the fusion of modalities, ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally, MPT serves as a unifying bridge, intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular, with a specific emphasis on the ocular region, yielding exceptional performance in both intra- and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the noteworthy performance of the FBR model. The source code is available at https://***/MIS-DevWorks/FBR.

关键词： cross-modality flexible biometrics recognition multimodal prompt tuning

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An Investigation into the Impact of AI-Powered Image Enhancement on Forensic Facial recognition

An Investigation into the Impact of AI-Powered Image Enhance...

引用

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

作者： Norman, Justin Farid, Hany Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798350365474

Advances in machine learning and computer vision have led to significant improvements in automated facial recognition. Many real-world forensic settings, however, are confronted with challenging low-quality and low-resolution images that often confound even state-of-the art facial recognition. We investigate if and when advances in neural-based image enhancement and restoration can be used to restore degraded images while preserving facial identity for use in forensic facial recognition.

关键词： Forensic Facial recognition Image Enhancement

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