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Learning Dual Convolutional Neural Networks for Low-Level vision 31

Learning Dual Convolutional Neural Networks for Low-Level Vi...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pan, Jinshan Liu, Sifei Sun, Deqing Zhang, Jiawei Liu, Yang Ren, Jimmy Li, Zechao Tang, Jinhui Lu, Huchuan Tai, Yu-Wing Yang, Ming-Hsuan Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China NVIDIA Bengaluru India City Univ Hong Kong Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China SenseTime Res Beijing Peoples R China Tencent Youtu Lab Shanghai Peoples R China UC Merced Merced CA USA

ISBN: (纸本)9781538664209

In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.

关键词： Image resolution Task analysis Atmospheric modeling Signal resolution Computational modeling Convolutional neural networks Visualization

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3D Video Object Detection with Learnable Object-Centric Global Optimization

3D Video Object Detection with Learnable Object-Centric Glob...

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

作者： He, Jiawei Chen, Yuntao Wang, Naiyan Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat CRIPAC Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci HKISI Ctr Artificial Intelligence & Robot Beijing Peoples R China TuSimple San Diego CA USA

ISBN: (纸本)9798350301298

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and feature-metric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://***/jiaweihe1996/BA-Det.

关键词： detection recognition: Categorization retrieval

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Injecting Semantic Concepts into End-to-End Image Captioning

Injecting Semantic Concepts into End-to-End Image Captioning

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

作者： Fang, Zhiyuan Wang, Jianfeng Hu, Xiaowei Liang, Lin Gan, Zhe Wang, Lijuan Yang, Yezhou Liu, Zicheng Arizona State Univ Tempe AZ 85287 USA Microsoft Corp Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Tremendous progresses have been made in recent years in developing better image captioning models, yet most of them rely on a separate object detector to extract regional features. Recent vision-language studies are shifting towards the detector-free trend by leveraging grid representations for more flexible model training and faster inference speed. However, such development is primarily focused on image understanding tasks, and remains less investigated for the caption generation task. In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as 14TCAP, in which grid representations are used without extracting the regional features. For improved performance, we introduce a novel Concept Token Network (CM) to predict the semantic concepts and then incorporate them into the end-to-end captioning. In particular, the CTN is built on the basis of a vision transformer, and is designed to predict the concept tokens through a classification task, from which the rich semantic information contained greatly benefits the captioning task. Compared with the previous detector-based models, 14TCAP drastically simplifies the architectures and at the same time achieves competitive performance on various challenging image captioning datasets. In particular, ViTCAP reaches 138.1 CIDEr scores on COCO-caption Karpathy-split, 93.8 and 108.6 CIDEr scores on nocaps and Google-CC captioning datasets, respectively.

关键词： Training Computational modeling Semantics computer architecture Feature extraction Transformers Market research

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Learning Neural Light Fields with Ray-Space Embedding

Learning Neural Light Fields with Ray-Space Embedding

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

作者： Attal, Benjamin Huang, Jia-Bin Zollhofer, Michael Kopf, Johannes Kim, Changil Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta Menlo Pk CA USA Real Labs Res Pittsburgh PA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow to render, requiring hundreds of network evaluations per pixel to approximate a volume rendering integral. Baking NeRFs into explicit data structures enables efficient rendering, but results in large memory footprints and, in some cases, quality reduction. Additionally, volumetric representations for view synthesis often struggle to represent challenging view dependent effects such as distorted reflections and refractions. We present a novel neural light field representation that, in contrast to prior work, is fast, memory efficient, and excels at modeling complicated view dependence. Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines. At the core of our approach is a ray-space embedding network that maps 4D ray-space into an intermediate, interpolable latent space. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset. In addition, for forward-facing scenes with sparser inputs we achieve results that are competitive with NeRF-based approaches while providing a better speed/quality/memory trade-off with far fewer network evaluations.

关键词： Photography Interpolation Three-dimensional displays Memory management Rendering (computer graphics) Light fields Reflection

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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3D Human Pose Perception from Egocentric Stereo Videos

3D Human Pose Perception from Egocentric Stereo Videos

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

作者： Akada, Hiroyasu Wang, Jian Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2, UnrealEgo-RW, and trained models are available on our project page(1) and Benchmark Challenge(2).

关键词： Egocentric 3D human pose estimation First-person view Stereo vision

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Cascaded Partial Decoder for Fast and Accurate Salient Object Detection 32

Cascaded Partial Decoder for Fast and Accurate Salient Objec...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Zhe Su, Li Huang, Qingming Univ Chinese Acad Sci UCAS Sch Comp Sci & Technol Beijing Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9781728132938

Existing state-of-the-art salient object detection networks rely on aggregating multi-level features of pretrained convolutional neural networks (CNNs). Compared to high-level features, low-level features contribute less to performance but cost more computations because of their larger spatial resolutions. In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection. On the one hand, the framework constructs partial decoder which discards larger resolution features of shallower layers for acceleration. On the other hand, we observe that integrating features of deeper layers obtain relatively precise saliency map. Therefore we directly utilize generated saliency map to refine the features of backbone network This strategy efficiently suppresses distractors in the features and significantly improves their representation ability. Experiments conducted on five benchmark datasets exhibit that the proposed model not only achieves state-of-the-art performance but also runs much faster than existing models. Besides, the proposed framework is further applied to improve existing multi-level feature aggregation models and significantly improve their efficiency and accuracy.

关键词： Grouping and Shape Low-level vision Segmentation

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Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

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

作者： Sun, Zeyi Fang, Ye Wu, Tong Zhang, Pan Zang, Yuhang Kong, Shu Xiong, Yuanjun Lin, Dahua Wang, Jiaqi Shanghai Jiao Tong Univ Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Macau Taipa Macao Peoples R China MThreads Inc Beijing Peoples R China

ISBN: (纸本)9798350353006

Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models. To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks. Our project is with codes and models available is linked to https://***/alpha-clip/.

关键词： CLIP MLLMs vision-language pretraining

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Contrastive Regression for Domain Adaptation on Gaze Estimation

Contrastive Regression for Domain Adaptation on Gaze Estimat...

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

作者： Wang, Yaoming Jiang, Yangzhou Li, Jin Ni, Bingbing Dai, Wenrui Li, Chenglin Xiong, Hongkai Li, Teng Shanghai Jiao Tong Univ Shanghai Peoples R China Huawei Inc Shenzhen Peoples R China Anhui Univ Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large domain gap hinders the performance of trained models on new domains. In this paper, we propose a novel gaze adaptation approach, namely Contrastive Regression Gaze Adaptation (CRGA), for generalizing gaze estimation on the target domain in an unsupervised manner. CRGA leverages the Contrastive Domain Generalization (CDG) module to learn the stable representation from the source domain and leverages the Contrastive Self-training Adaptation (CSA) module to learn from the pseudo labels on the target domain. The core of both CDG and CSA is the Contrastive Regression (CR) loss, a novel contrastive loss for regression by pulling features with closer gaze directions closer together while pushing features with farther gaze directions farther apart. Experimentally, we choose ETH-XGAZE and Gaze-360 as the source domain and test the domain generalization and adaptation performance on MPIIGAZE, RT-GENE, Gaze-Capture, EyeDiap respectively. The results demonstrate that our CRGA achieves remarkable performance improvement compared with the baseline models and also outperforms the state-of-the-art domain adaptation approaches on gaze adaptation tasks.

关键词： Representation learning Deep learning Adaptation models computer vision Annotations Face recognition Neural networks

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Geometry-Aware Distillation for Indoor Semantic Segmentation 32

Geometry-Aware Distillation for Indoor Semantic Segmentation

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jiao, Jianbo Wei, Yunchao Jie, Zequn Shi, Honghui Lau, Rynson Huang, Thomas S. Univ Oxford Dept Engn Sci Oxford OX1 2JD England UIUC Champaign IL USA Tencent AI Lab Bellevue WA USA IBM Res Armonk NY USA City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

It has been shown that jointly reasoning the 2D appearance and 3D information from RGB-D domains is beneficial to indoor scene semantic segmentation. However, most existing approaches require accurate depth map as input to segment the scene which severely limits their applications. In this paper, we propose to jointly infer the semantic and depth information by distilling geometry-aware embedding to eliminate such strong constraint while still exploiting the helpful depth domain information. In addition, we use this learned embedding to improve the quality of semantic segmentation, through a proposed geometry-aware propagation framework followed by several multi-level skip feature fusion blocks. By decoupling the single task prediction network into two joint tasks of semantic segmentation and geometry embedding learning, together with the proposed information propagation and feature fusion architecture, our method is shown to perform favorably against state-of-the-art methods for semantic segmentation on publicly available challenging indoor datasets.

关键词： Categorization Deep Learning Grouping and Shape recognition: Detection Retrieval Scene Analysis and Understanding Segmentation

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