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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4461-4470 订阅

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Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

Deep RGB-D Saliency Detection with Depth-Sensitive Attention...

引用

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

作者： Sun, Peng Zhang, Wenhu Wang, Huanyu Li, Songyuan Li, Xi Zhejiang Univ Coll Comp Sci & Technol Shanghai Inst Adv Study Hangzhou Peoples R China

ISBN: (纸本)9781665445092

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.

关键词： Geometry Visualization computer vision computer architecture Object detection Benchmark testing pattern recognition

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Language Guided Local Infiltration for Interactive Image Retrieval

Language Guided Local Infiltration for Interactive Image Ret...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Fuxiang Huang Lei Zhang Learning Intelligence & Vision Essential (LiVE) Group School of Microelectronics and Communication Engineering Chongqing University China

Interactive Image Retrieval (IIR) aims to retrieve images that are generally similar to the reference image but under the requested text modification. The existing methods usually concatenate or sum the features of image and text simply and roughly, which, however, is difficult to precisely change the local semantics of the image that the text intends to modify. To solve this problem, we propose a Language Guided Local Infiltration (LGLI) system, which fully utilizes the text information and penetrates text features into image features as much as possible. Specifically, we first propose a Language Prompt Visual Localization (LPVL) module to generate a localization mask which explicitly locates the region (semantics) intended to be modified. Then we introduce a Text Infiltration with Local Awareness (TILA) module, which is deployed in the network to precisely modify the reference image and generate image-text infiltrated representation. Extensive experiments on various benchmark databases validate that our method outperforms most state-of-the-art IIR approaches.

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SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

SceneGraphFusion: Incremental 3D Scene Graph Prediction from...

引用

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

作者： Wu, Shun-Cheng Wald, Johanna Tateno, Keisuke Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.

关键词： computer vision Three-dimensional displays Simultaneous localization and mapping Fuses Semantics Prediction methods Graph neural networks

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Open World Compositional Zero-Shot Learning

Open World Compositional Zero-Shot Learning

引用

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

作者： Mancini, Massimiliano Naeem, Muhammad Ferjad Xian, Yongqin Akata, Zeynep Univ Tubingen Tubingen Germany Tech Univ Munich Munich Germany MPI Informat Saarbrucken Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665445092

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art. Code is available at:https://***/ExplainableML/czsl.

关键词： Training Degradation Visualization computer vision Computational modeling Knowledge based systems Benchmark testing

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Privacy Preserving Localization and Mapping from Uncalibrated Cameras

Privacy Preserving Localization and Mapping from Uncalibrate...

引用

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

作者： Geppert, Marcel Larsson, Viktor Speciale, Pablo Schonberger, Johannes L. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features.

关键词： Location awareness Performance evaluation Privacy Robot vision systems Pose estimation Poles and towers Cameras

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Cluster-wise Hierarchical Generative Model for Deep Amortized Clustering

Cluster-wise Hierarchical Generative Model for Deep Amortize...

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

作者： Liu, Huafeng Wang, Jiaqi Jing, Liping Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac). It provides an efficient neural clustering architecture by grouping data points in a cluster-wise view rather than point-wise view. CHiGac simultaneously learns what makes a cluster, how to group data points into clusters, and how to adaptively control the number of clusters. The dedicated cluster generative process is able to sufficiently exploit pairwise or higher-order interactions between data points in both inter- and intra-cluster, which is useful to sufficiently mine the hidden structure among data. To efficiently minimize the generalized lower bound of CHiGac, we design an Ergodic Amortized Inference (EAI) strategy by considering the average behavior over sequence on an inner variational parameter trajectory, which is theoretically proven to reduce the amortization gap. A series of experiments have been conducted on both synthetic and real-world data. The experimental results demonstrated that CHiGac can efficiently and accurately cluster datasets in terms of both internal and external evaluation metrics (DBI and ACC).

关键词： Measurement computer vision Adaptation models Computational modeling computer architecture Trajectory pattern recognition

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Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

Fashion IQ: A New Dataset Towards Retrieving Images by Natur...

引用

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

作者： Wu, Hui Gao, Yupeng Guo, Xiaoxiao Al-Halah, Ziad Rennie, Steven Grauman, Kristen Feris, Rogerio MIT IBM Watson AI Lab Cambridge MA 02142 USA IBM Res Armonk NY 10504 USA UT Austin Austin TX USA Pryon New York NY USA

ISBN: (纸本)9781665445092

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialogbased image retrieval. We believe that our dataset will encourage further work on developing more natural and realworld applicable conversational shopping assistants.(1)

关键词： Visualization computer vision Image retrieval Natural languages Clothing Buildings Ontologies

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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ali Nasiri-Sarvi Vincent Quoc-Huy Trinh Hassan Rivaz Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE) Concordia University Canada Institute for Research in Immunology and Cancer of the University of Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim’s enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim’s capabilities, which reveals that Vim uniquely emulates the pathologist workflow—unlike ViT. This alignment with human expert analysis highlights Vim’s potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https://***/AtlasAnalyticsLab/Vim4Path.

关键词： Representation learning Training computer vision Adaptation models Computational modeling Self-supervised learning computer architecture

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StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

StillFast: An End-to-End Approach for Short-Term Object Inte...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Francesco Ragusa Giovanni Maria Farinella Antonino Furnari FPV@IPLab University of Catania Next Vision s.r.l. - Spin-off of the University of Catania

Anticipation problem has been studied considering different aspects such as predicting humans’ locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://***/stillfast/.

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AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

AttentiveNAS: Improving Neural Architecture Search via Atten...

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

作者： Wang, Dilin Li, Meng Gong, Chengyue Chandra, Vikas Facebook Menlo Pk CA 94025 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models. While uniform sampling has been widely used for its simplicity, it is agnostic of the model performance Pareto front, which is the main focus in the search process, and thus, misses opportunities to further improve the model accuracy. In this work, we propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto. We also propose algorithms to efficiently and effectively identify the networks on the Pareto during training. Without extra re-training or post-processing, we can simultaneously obtain a large number of networks across a wide range of FLOPs. Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS, Once-for-All networks and FBNetV3. We also achieve ImageNet accuracy of 80.1% with only 491 MFLOPs. Our training code and pretrained models are available at https://***/facebookresearch/AttentiveNAS.

关键词： Training computer vision Codes computer architecture pattern recognition

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