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共 20950 条记录，以下是741-750 订阅

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LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

LVQAC: Lattice Vector Quantization Coupled with Spatially Ad...

引用

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

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity.

关键词： Low-level vision

来源：评论

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GRES: Generalized Referring Expression Segmentation

GRES: Generalized Referring Expression Segmentation

引用

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

作者： Liu, Chang Ding, Henghui Jiang, Xudong Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object described by a given language expression. Existing classic RES datasets and methods commonly support single-target expressions only, i.e., one expression refers to one target object. Multi-target and no-target expressions are not considered. This limits the usage of RES in practice. In this paper, we introduce a new benchmark called Generalized Referring Expression Segmentation (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects. Towards this, we construct the first largescale GRES dataset called gRefCOCO that contains multitarget, no-target, and single-target expressions. GRES and gRefCOCO are designed to be well-compatible with RES, facilitating extensive experiments to study the performance gap of the existing RES methods on the GRES task. In the experimental study, we find that one of the big challenges of GRES is complex relationship modeling. Based on this, we propose a region-based GRES baseline ReLA that adaptively divides the image into regions with subinstance clues, and explicitly models the region-region and region-language dependencies. The proposed approach ReLA achieves new state-of-the-art performance on the both newly proposed GRES and classic RES tasks. The proposed gRefCOCO dataset and method are available at https://***/GRES.

关键词： language reasoning vision

来源：评论

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Data-driven Feature Tracking for Event Cameras

Data-driven Feature Tracking for Event Cameras

引用

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

作者： Messikommer, Nico Fang, Carter Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.

关键词： Low-level vision

来源：评论

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Potential Risk Localization via Weak Labeling out of Blind Spot

Potential Risk Localization via Weak Labeling out of Blind S...

引用

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

作者： Shimomura, Kota Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

Achieving fully autonomous driving requires not only understanding the current surrounding conditions but also predicting how objects that could lead to potential risks may change in the future. Predicting potential risk regions, especially where pedestrians or vehicles might suddenly appear, is crucial for safe autonomous driving and accident avoidance. Constructing datasets annotated with potential risk regions is costly. Therefore, conventional methods have proposed blind spot estimation using depth maps or segmentation masks through automatic labeling. However, these methods are limited in applicability due to their reliance on camera parameters or point clouds. In this study, we propose a method to automatically generate labels from depth maps and segmentation masks and estimate potential risk regions in 2D. Our automatic labeling algorithm relies solely on images, making it applicable to all onboard camera datasets. To demonstrate the effectiveness of our approach, we define regions where pedestrians or vehicles might emerge from blind spots as potential risk regions and annotate them to create a new dataset extended with potential risk region annotations. Experiments using the Cityscapes Dataset show that weakly training with labels generated by our proposed method achieves equal or superior accuracy compared with supervised training with manually annotated ground truth (GT). Furthermore, experiments using the Mapillary Vistas Dataset and BDD100K Dataset demonstrate the versatility of our approach.

关键词： Advanced Driver-Assistance Systems Autonomous Driving computer vision Deep Learning

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Generative Bias for Robust Visual Question Answering

Generative Bias for Robust Visual Question Answering

引用

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

作者： Cho, Jae Won Kim, Dong-Jin Ryu, Hyeonggon Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust target model. However, these methods compute the bias for a model simply from the label statistics of the training data or from single modal branches. In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. In particular, GenB employs a generative network to learn the bias in the target model through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT architecture on VQA-CP2.

关键词： language reasoning vision

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Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action recognition

Actionlet-Dependent Contrastive Learning for Unsupervised Sk...

引用

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

作者： Lin, Lilang Zhang, Jiahang Liu, Jiaying Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on the accuracy of action recognition. To realize the adaptive action modeling of both parts, we propose an Actionlet-Dependent Contrastive Learning method (ActCLR). The actionlet, defined as the discriminative subset of the human skeleton, effectively decomposes motion regions for better action modeling. In detail, by contrasting with the static anchor without motion, we extract the motion region of the skeleton data, which serves as the actionlet, in an unsupervised manner. Then, centering on actionlet, a motion-adaptive data transformation method is built. Different data transformations are applied to actionlet and non-actionlet regions to introduce more diversity while maintaining their own characteristics. Meanwhile, we propose a semantic-aware feature pooling method to build feature representations among motion and static regions in a distinguished manner. Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method achieves remarkable action recognition performance. More visualization and quantitative experiments demonstrate the effectiveness of our method. Our project website is available at https://***/projects/ActCLR/

关键词： Video: Action and event understanding

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SelfME: Self-Supervised Motion Learning for Micro-Expression recognition

SelfME: Self-Supervised Motion Learning for Micro-Expression...

引用

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

作者： Fan, Xinqi Chen, Xueli Jiang, Mingjie Shahid, Ali Raza Yan, Hong City Univ Hong Kong Hong Kong Peoples R China COMSATS Univ Islamabad Islamabad Pakistan

ISBN: (纸本)9798350301298

Facial micro-expressions (MEs) refer to brief spontaneous facial movements that can reveal a person's genuine emotion. They are valuable in lie detection, criminal analysis, and other areas. While deep learning-based ME recognition (MER) methods achieved impressive success, these methods typically require pre-processing using conventional optical flow-based methods to extract facial motions as inputs. To overcome this limitation, we proposed a novel MER framework using self-supervised learning to extract facial motion for ME (SelfME). To the best of our knowledge, this is the first work using an automatically self-learned motion technique for MER. However, the self-supervised motion learning method might suffer from ignoring symmetrical facial actions on the left and right sides of faces when extracting fine features. To address this issue, we developed a symmetric contrastive vision transformer (SCViT) to constrain the learning of similar facial action features for the left and right parts of faces. Experiments were conducted on two benchmark datasets showing that our method achieved state-of-the-art performance, and ablation studies demonstrated the effectiveness of our method.

关键词： body gesture Humans: Face movement pose

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Ambiguous Medical Image Segmentation using Diffusion Models

Ambiguous Medical Image Segmentation using Diffusion Models

引用

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

作者： Rahman, Aimon Valanarasu, Jeya Maria Jose Hacihaliloglu, Ilker Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://***/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.

关键词： cell microscopy Medical and biological vision

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MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

MetaFusion: Infrared and Visible Image Fusion via Meta-Featu...

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

作者： Zhao, Wenda Xie, Shigeng Zhao, Fan He, You Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Liaoning Normal Univ Dalian Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://***/wdzhao123/MetaFusion.

关键词： Low-level vision

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Probabilistic Prompt Learning for Dense Prediction

Probabilistic Prompt Learning for Dense Prediction

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

作者： Kwon, Hyeongjun Song, Taeyong Jeong, Somi Kim, Jin Jang, Jinhyun Sohn, Kwanghoon Yonsei Univ Seoul South Korea Hyundai Motor Co R&D Div Seoul South Korea NAVER LABS Seongnam South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method.

关键词： and reasoning language vision

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