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Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction

Semi-Supervised Video Semantic Segmentation with Inter-Frame...

引用

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

作者： Zhuang, Jiafan Wang, Zilei Gao, Yuan Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

One major challenge for semantic segmentation in real-world scenarios is only limited pixel-level labels available due to high expense of human labor though a vast volume of video data is provided. Existing semi-supervised methods attempt to exploit unlabeled data in model training, but they just regard video as a set of independent images. To better explore semi-supervised segmentation problem with video data, we formulate a semi-supervised video semantic segmentation task in this paper. For this task, we observe that the overfitting is surprisingly severe between labeled and unlabeled frames within a training video although they are very similar in style and contents. This is called inner-video overfitting, and it would actually lead to inferior performance. To tackle this issue, we propose a novel interframe feature reconstruction (IFR) technique to leverage the ground-truth labels to supervise the model training on unlabeled frames. IFR is essentially to utilize the internal relevance of different frames within a video. During training, IFR would enforce the feature distributions between labeled and unlabeled frames to be narrowed. Consequently, the inner-video overfitting issue can be effectively alleviated. We conduct extensive experiments on Cityscapes and CamVid, and the results demonstrate the superiority of our proposed method to previous state-of-the-art methods. The code is available at https: / /***/jfzhuang/IFR.

关键词： Training Image segmentation computer vision Codes Semantics Supervised learning Data models

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FocusCut: Diving into a Focus View in Interactive Segmentation

FocusCut: Diving into a Focus View in Interactive Segmentati...

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

作者： Lin, Zheng Duan, Zheng-Peng Zhang, Zhao Guo, Chun-Le Cheng, Ming-Ming Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interactive image segmentation is an essential tool in pixel-level annotation and image editing. To obtain a high-precision binary segmentation mask, users tend to add interaction clicks around the object details, such as edges and holes, for efficient refinement. Current methods regard these repair clicks as the guidance to jointly determine the global prediction. However, the global view makes the model lose focus from later clicks, and is not in line with user intentions. In this paper, we dive into the view of clicks' eyes to endow them with the decisive role in object details again. To verify the necessity of focus view, we design a simple yet effective pipeline, named FocusCut, which integrates the functions of object segmentation and local refinement. After obtaining the global prediction, it crops click-centered patches from the original image with adaptive scopes to refine the local predictions progressively. Without user perception and parameters increase, our method has achieved state-of-the-art results. Extensive experiments and visualized results demonstrate that FocusCut makes hyper fine segmentation possible for interactive image segmentation.

关键词： Image segmentation Visualization computer vision Annotations Image edge detection Pipelines Crops

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PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationshi...

引用

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

作者： Zhang, Tianyi Lin, Jie Hu, Peng Zhao, Bin Aly, Mohamed M. Sabry ASTAR I2R Singapore Singapore Sichuan Univ Chengdu Peoples R China ASTAR IME Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.

关键词： computer vision Pipelines Detectors Object detection Hardware pattern recognition Convolutional neural networks

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Strengthen Learning Tolerance for Weakly Supervised Object Localization

Strengthen Learning Tolerance for Weakly Supervised Object L...

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

作者： Guo, Guangyu Han, Junwei Wan, Fang Zhang, Dingwen Northwestern Polytech Univ Brain & Artificial Intelligence Lab Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning robustness issue. Specifically, the former makes the localizer prone to the local discriminative object regions rather than the desired whole object, and the latter makes the localizer over-sensitive to the variations of the input images so that one can hardly obtain localization results robust to the arbitrary visual stimulus. To solve these issues, we propose a novel framework to strengthen the learning tolerance, referred to as SLT-Net, for WSOL. Specifically, we consider two fold learning tolerance strengthening mechanisms. One is the semantic tolerance strengthening mechanism, which allows the localizer to make mistakes for classifying similar semantics so that it will not concentrate too much on the discriminative local regions. The other is the visual stimuli tolerance strengthening mechanism, which enforces the localizer to be robust to different image transformations so that the prediction quality will not be sensitive to each specific input image. Finally, we implement comprehensive experimental comparisons on two widely-used datasets CUB and ILSVRC2012, which demonstrate the effectiveness of our proposed approach.

关键词： Location awareness Visualization computer vision Computational modeling Semantics Robustness pattern recognition

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Temporally Efficient vision Transformer for Video Instance Segmentation

Temporally Efficient Vision Transformer for Video Instance S...

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

作者： Yang, Shusheng Wang, Xinggang Li, Yu Fang, Yuxin Fang, Jiemin Liu, Wenyu Zhao, Xun Shan, Ying Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Hubei Peoples R China Tencent PCG Appl Res Ctr ARC London England Int Digital Econ Acad IDEA Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently vision transformer has achieved tremendous success on image-level visual recognition tasks. To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient vision Transformer (TeViT) for video instance segmentation (VIS). Different from previous transformer-based VIS methods, TeViT is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head. In the backbone stage, we propose a nearly parameter-free messenger shift mechanism for early temporal context fusion. In the head stages, we propose a parameter-shared spatiotemporal query interaction mechanism to build the one-to-one correspondence between video instances and queries. Thus, TeViT fully utilizes both frame-level and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost. On three widely adopted VIS benchmarks, i.e., YouTube-VIS-2019, YouTube-VIS-2021, and OVIS, TeViT obtains state-of-the-art results and maintains high inference speed, e.g., 46.6 AP with 68.9 FPS on YouTube-VIS-2019. Code is available at https://***/hustvl/TeViT.

关键词： Image segmentation Visualization Head Shape Transformers Spatiotemporal phenomena Computational efficiency

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Progressive Teacher-student Learning for Early Action Prediction 32

Progressive Teacher-student Learning for Early Action Predic...

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

作者： Wang, Xionghui Hu, Jian-Fang Lai, Jianhuang Zhang, Jianguo Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Univ Dundee Dundee Scotland Guangdong Prov Key Lab Informat Secur Technol Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728132938

The goal of early action prediction is to recognize actions from partially observed videos with incomplete action executions, which is quite different from action recognition. Predicting early actions is very challenging since the partially observed videos do not contain enough action information for recognition. In this paper, we aim at improving early action prediction by proposing a novel teacher-student learning framework. Our framework involves a teacher model for recognizing actions from full videos, a student model for predicting early actions from partial videos, and a teacher-student learning block for distilling progressive knowledge from teacher to student, crossing different tasks. Extensive experiments on three public action datasets show that the proposed progressive teacher-student learning framework can consistently improve performance of early action prediction model. We have also reported the state-of-the-art performances for early action prediction on all of these sets.

关键词： Action recognition Deep Learning Video Analytics vision Applications and Systems

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PIGEON: Predicting Image Geolocations

PIGEON: Predicting Image Geolocations

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

作者： Haas, Lukas Skreta, Michal Alberti, Silas Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization;the first model, PIGEON, is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.(1)

关键词： Blind Experiment CLIP Clustering computer vision GeoGuessr Haversine Image Geolocalization Location Refinement Multi-Modal Multi-Task Multi-Task Pretraining Photo Geolocalization Pretraining Retrieval Semantic Geocells Visual Place recognition Voronoi

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Weakly Supervised Dense Video Captioning 30

Weakly Supervised Dense Video Captioning

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shen, Zhiqiang Li, Jianguo Su, Zhou Li, Minjun Chen, Yurong Jiang, Yu-Gang Xue, Xiangyang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781538604571

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video regionsequence correspondence, but is only based on weak videolevel sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (LexicalFCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse regionsequences based on the Lexical-FCN outputs. A winnertakes- all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequenceto- sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.

关键词： Visualization Training Motion segmentation Vocabulary computer vision Neural networks captions Motion segmentation images computer vision Neural network Visualization Vocabulary Functional training Videotapes video clips Training modelling languages

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A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres

A Brand New Dance Partner: Music-Conditioned Pluralistic Dan...

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

作者： Kim, Jinwoo Oh, Heeseok Kim, Seongjean Tong, Hoseok Lee, Sanghoon Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Hansung Univ Dept Appl AI Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dance-genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. We conduct extensive experiments on AIST++ along with user studies. Compared to the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates outperforming dance sequences qualitatively and quantitatively.

关键词： Training Measurement Humanities computer vision Computational modeling computer architecture Transformers

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Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Informative and Consistent Correspondence Mining for Cross-D...

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

作者： Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.

关键词： computer vision Annotations Collaboration Object detection Detectors Generators pattern recognition

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