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PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes

PlaneDepth: Self-supervised Depth Estimation via Orthogonal ...

引用

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

作者： Wang, Ruoyu Yu, Zehao Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Univ Tubingen Tubingen Germany Shanghai Engn Res Ctr Intelligent Vision & Imagin Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom I Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MDE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the PlaneDepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://***/svip-lab/PlaneDepth.

关键词： 3D from multi-view and sensors

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Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses

Unsupervised Contour Tracking of Live Cells by Mechanical an...

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

作者： Jang, Junbong Lee, Kwonmoo Kim, Tae-Kyun Korea Adv Inst Sci & Technol Seoul South Korea Harvard Med Sch Boston Childrens Hosp Boston MA 02115 USA Imperial Coll London London England

ISBN: (纸本)9798350301298

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://***/JunbongJang/ contour-tracking/

关键词： cell microscopy Medical and biological vision

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Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration

Bitstream-Corrupted JPEG Images are Restorable: Two-stage Co...

引用

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

作者： Liu, Wenyang Wang, Yi Yap, Kim-Hui Chau, Lap-Pui Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9798350301298

In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream. The bit errors bring unpredictable color casts and block shifts on decoded image contents, which cannot be resolved by existing image restoration methods mainly relying on pre-defined degradation models in the pixel domain. To address these challenges, we propose a robust JPEG decoder, followed by a two-stage compensation and alignment framework to restore bitstream-corrupted JPEG images. Specifically, the robust JPEG decoder adopts an error-resilient mechanism to decode the corrupted JPEG bitstream. The two-stage framework is composed of the self-compensation and alignment (SCA) stage and the guided-compensation and alignment (GCA) stage. The SCA adaptively performs block-wise image color compensation and alignment based on the estimated color and block offsets via image content similarity. The GCA leverages the extracted low-resolution thumbnail from the JPEG header to guide full-resolution pixel-wise image restoration in a coarse-to-fine manner. It is achieved by a coarse-guided pix2pix network and a refine-guided bi-directional Laplacian pyramid fusion network. We conduct experiments on three benchmarks with varying degrees of bit error rates. Experimental results and ablation studies demonstrate the superiority of our proposed method. The code will be released at https://***/wenyang001/Two- ACIR.

关键词： Low-level vision

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SemiCVT: Semi-Supervised Convolutional vision Transformer for Semantic Segmentation

SemiCVT: Semi-Supervised Convolutional Vision Transformer fo...

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

作者： Huang, Huimin Xie, Shiao Lin, Lanfen Tong, Ruofeng Chen, Yen-Wei Li, Yuexiang Wang, Hong Huang, Yawen Zheng, Yefeng Zhejiang Univ Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Ritsumeikan Univ Kyoto Japan Tencent Jarvis Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Transformer achieves superior performance on the entire feature map in various tasks. In this paper, we unify the current dominant Mean-Teacher approaches by reconciling intramodel and inter-model properties for semi-supervised segmentation to produce a novel algorithm, SemiCVT, that absorbs the quintessence of CNNs and Transformer in a comprehensive way. Specifically, we first design a parallel CNN-Transformer architecture (CVT) with introducing an intra-model local-global interaction schema (LGI) in Fourier domain for full integration. The inter-model class-wise consistency is further presented to complement the class-level statistics of CNNs and Transformer in a cross-teaching manner. Extensive empirical evidence shows that SemiCVT yields consistent improvements over the state-of-the-art methods in two public benchmarks.

关键词： grouping and shape analysis Segmentation

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SAP-DETR: Bridging the Gap between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency

SAP-DETR: Bridging the Gap between Salient Points and Querie...

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

作者： Liu, Yang Zhang, Yao Wang, Yixin Zhang, Yang Tian, Jiang Shi, Zhongchao Fan, Jianping He, Zhiqiang Chinese Acad Sci Inst Comp Technol ICT Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Univ Chinese Acad Sci Beijing Peoples R China Lenovo Res AI Lab Beijing Peoples R China Lenovo Ltd Beijing Peoples R China

ISBN: (纸本)9798350301298

Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerating Transformer detector convergency. These methods gradually refine the reference points to the center of target objects and imbue object queries with the updated central reference information for spatially conditional attention. However, centralizing reference points may severely deteriorate queries' saliency and confuse detectors due to the indiscriminative spatial prior. To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects. Concretely, we explicitly initialize a query-specific reference point for each object query, gradually aggregate them into an instance object, and then predict the distance from each side of the bounding box to these points. By rapidly attending to query-specific reference regions and the conditional box edges, SAP-DETR can effectively bridge the gap between the salient point and the query-based Transformer detector with a significant convergency speed. Experimentally, SAP-DETR achieves 1.4x convergency speed with competitive performance and stably promotes the SoTA approaches by similar to 1.0 AP. Based on ResNet-DC-101, SAP-DETR achieves 46.9 AP. The code will be released at https://***/liuyang-ict/SAP-DETR.

关键词： detection recognition: Categorization retrieval

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RangeViT: Towards vision Transformers for 3D Semantic Segmentation in Autonomous Driving

RangeViT: Towards Vision Transformers for 3D Semantic Segmen...

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

作者： Ando, Angelika Gidaris, Spyros Bursuc, Andrei Puy, Gilles Boulch, Alexandre Marlet, Renaud Valeo Ai Paris France Univ PSL Mines Paris Ctr Robot Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9798350301298

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image-based benchmarks. In this work, we question if projection-based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://***/valeoai/rangevit.

关键词： Deep learning architectures and techniques

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Text with Knowledge Graph Augmented Transformer for Video Captioning

Text with Knowledge Graph Augmented Transformer for Video Ca...

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

作者： Gu, Xin Chen, Guang Wang, Yufei Zhang, Libo Luo, Tiejian Wen, Longyin Univ Chinese Acad Sci Beijing Peoples R China ByteDance Inc San Jose CA USA Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9798350301298

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb additional knowledge, which models the interactions between the additional knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the long-tail words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e.g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results. In addition, the cross attention mechanism is also used in between the two streams for sharing information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSR-VTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method outperforms the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset.

关键词： language reasoning vision

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Deep Hashing with Minimal-Distance-Separated Hash Centers

Deep Hashing with Minimal-Distance-Separated Hash Centers

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

作者： Wang, Liangdao Pan, Yan Liu, Cong Lai, Hanjiang Yin, Jian Liu, Ye Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Lizhi Inc Big Data Dept Beijing Peoples R China

ISBN: (纸本)9798350301298

Deep hashing is an appealing approach for large-scale image retrieval. Most existing supervised deep hashing methods learn hash functions using pairwise or triple image similarities in randomly sampled mini-batches. They suffer from low training efficiency, insufficient coverage of data distribution, and pair imbalance problems. Recently, central similarity quantization (CSQ) attacks the above problems by using "hash centers" as a global similarity metric, which encourages the hash codes of similar images to approach their common hash center and distance themselves from other hash centers. Although achieving SOTA retrieval performance, CSQ falls short of a worst-case guarantee on the minimal distance between its constructed hash centers, i.e. the hash centers can be arbitrarily close. This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the nonconvex nature of the problem. More importantly, we adopt the Gilbert-Varshamov bound from coding theory, which helps us to obtain a large minimal distance while ensuring the empirical feasibility of our optimization approach. With these clearly-separated hash centers, each is assigned to one image class, we propose several effective loss functions to train deep hashing networks. Extensive experiments on three datasets for image retrieval demonstrate that the proposed method achieves superior retrieval performance over the state-of-the-art deep hashing methods.

关键词： detection recognition: Categorization retrieval

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Hard Patches Mining for Masked Image Modeling

Hard Patches Mining for Masked Image Modeling

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

作者： Wang, Haochen Song, Kaiyou Fan, Junsong Wang, Yuxi Xie, Jin Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China Megvii Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct.(1)

关键词： Self-supervised or unsupervised representation learning

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You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified vision Language Model

You Need Multiple Exiting: Dynamic Early Exiting for Acceler...

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

作者： Tang, Shengkun Wang, Yaqing Kong, Zhenglun Zhang, Tianchi Li, Yao Ding, Caiwen Wang, Yanzhi Liang, Yi Xu, Dongkuan North Carolina State Univ Raleigh NC 27695 USA Google Res New York NY USA Univ Connecticut Mansfield PA USA Univ Michigan Ann Arbor MI USA Univ N Carolina Chapel Hill NC USA Northeastern Univ Boston MA USA

ISBN: (纸本)9798350301298

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full computation of the large-scale model, not all of inputs need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot be applied to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder layers. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To address this issue, we propose a novel early exiting strategy for unified vision language models, which allows to dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely MuE. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce expected inference time by up to 50% and 40% while maintaining 99% and 96% performance respectively.

关键词： Efficient and scalable vision

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