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Occlusion Guided Scene Flow Estimation on 3D Point Clouds

Occlusion Guided Scene Flow Estimation on 3D Point Clouds

引用

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

作者： Ouyang, Bojun Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665448994

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.(1,2)

关键词： Symbiosis Measurement Deep learning Three-dimensional displays Estimation computer architecture Tools

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Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Se...

引用

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

作者： Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.

关键词： Training Image segmentation computer vision conferences Semantics Minimization pattern recognition

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NL-FFC: Non-Local Fast Fourier Convolution for Image Super Resolution

NL-FFC: Non-Local Fast Fourier Convolution for Image Super R...

引用

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high resolution image. However, most of the approaches learns to upsample by using convolution in spatial domain and are confined to local features. This results into restricting the receptive field of the network and therefore deteriorates the overall quality of the high-resolution image. To alleviate this issue, we propose an architecture that learns both local and global features, and fuses them together to generate high quality images. The network uses a non-local attention aided Fast Fourier Convolutions (NL-FFC) to widen the receptive field and learn long-range dependencies. The analyses further show that these Fourier features implicitly provide faster convergence on low frequency components only to learn prior for unobserved high frequency components. The model generalizes well to different datasets. We further investigate the role of non-local attention, and the ratio of local and global features to maximize the performance gain in the ablation study.

关键词： Deep learning Convolution Fuses Superresolution Neural networks Performance gain pattern recognition

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Efficient Tensor Voting with 3D Tensorial Harmonics

Efficient Tensor Voting with 3D Tensorial Harmonics

引用

IEEE conference on computer vision and pattern recognition

作者： Reisert, Marco Burkhardt, Hans Univ Freiburg D-79110 Freiburg Germany

ISBN: (纸本)9781424423392

Tensor Voting is a robust technique to extract low-level features in noisy images. The approach achieves its robustness by exploiting coherent orientations in local neighborhoods. In this paper we propose an efficient algorithm for dense Tensor Voting in 3D which makes use of steerable filters. Therefore, we propose steerable expansions of spherical tensor fields in terms of tensorial harmonics, which are their canonical representation. In this way it is possible to perform arbitrary rank Tensor Voting by linear combinations of convolutions in an efficient way.

关键词： pattern recognition

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Unstructured Object Matching using Co-Salient Region Segmentation

Unstructured Object Matching using Co-Salient Region Segment...

引用

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

作者： Stoian, Ioana-Sabina Sandu, Ionut-Catalin Voinea, Daniel Popa, Alin-Ionut Amazon Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unstructured object matching is a less-explored and very challenging topic in the scientific literature. This includes matching scenarios where the context, appearance and the geometrical integrity of the objects to be matched changes drastically from one image to another (e.g. a pair of pyjamas which in one image is folded and in the other is worn by a person), making it impossible to determine a transformation which aligns the matched regions. Traditional approaches like keypoint-based feature matching perform poorly on this use case due to the high complexity in terms of viewpoint, scene context variety, background variations or high degrees of freedom concerning structural configurations. In this paper we propose a deep learning framework consisting of a twins based matching approach leveraging a co-salient region segmentation task and a cosine-similarity based region descriptor pairing technique. The importance of our proposed framework is demonstrated on a novel use case consisting of image pairs with various objects used by children. Additionally, we evaluate on Human3.6M and Market-1501, two datasets with humans depicting various appearances and kinematic configurations captured under different backgrounds.

关键词： Deep learning Image segmentation computer vision conferences Kinematics Search problems pattern recognition

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

引用

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

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Compact and Effective Representations for Sketch-based Image Retrieval

Compact and Effective Representations for Sketch-based Image...

引用

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

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences Image retrieval Memory management Search engines pattern recognition

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Multi-scale Grouped Dense Network for VVC Intra Coding

Multi-scale Grouped Dense Network for VVC Intra Coding

引用

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

作者： Li, Xin Sun, Simeng Zhang, Zhizheng Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams "IMC" and "haha" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.

关键词： Image coding conferences Generative adversarial networks Video coding Transform coding computer vision pattern recognition

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Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

Self-Supervised Learning of Remote Sensing Scene Representat...

引用

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

作者： Stojnic, Vladan Risojevic, Vladimir Univ Banja Luka Fac Elect Engn Banja Luka Bosnia & Herceg

ISBN: (纸本)9781665448994

In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks.

关键词： Visualization Image segmentation Image coding conferences Object detection Feature extraction pattern recognition

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Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

Efficient Space-time Video Super Resolution using Low-Resolu...

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

作者： Dutta, Saikat Shah, Nisarg A. Mittal, Anurag IIT Madras Chennai Tamil Nadu India IIT Jodhpur Karwar Rajasthan India

ISBN: (纸本)9781665448994

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A simplistic solution is the sequential running of Video Super Resolution and Video Frame interpolation models. However, this type of solutions are memory inefficient, have high inference time, and could not make the proper use of space-time relation property. To this extent, we first interpolate in LR space using quadratic modeling. Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps and blending mask which are used to synthesize LR interpolated frame is reused in HR space using bilinear upsampling. This leads to a coarse estimate of HR intermediate frame which often contains artifacts along motion boundaries. We use a refinement network to improve the quality of HR intermediate frame via residual learning. Our model is lightweight and performs better than current state-of-the-art models in REDS STSR Validation set.

关键词： Interpolation computer vision conferences Superresolution Refining pattern recognition Videos

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