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Mining and Unifying Heterogeneous Contrastive Relations for Weakly-Supervised Actor-Action Segmentation

Mining and Unifying Heterogeneous Contrastive Relations for ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Duan, Bin Tang, Hao Sun, Changchang Zhu, Ye Yan, Yan IIT Chicago IL 60616 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350318920;9798350318937

We introduce a novel weakly-supervised video actor-action segmentation (VAAS) framework, where only video-level tags are available. Previous VAAS methods follow a synthesize-and-refine scheme, i.e., they first synthesize the pseudo-segmentation and recursively refine the segmentation. However, this process requires significant time costs and heavily relies on the quality of the initial segmentation. Unlike existing works, our method hierarchically mines contrastive relations to supplement each other for learning a visually-plausible segmentation model. Specifically, three contrastive relations are abstracted from the pixel-level and frame-level, i.e., low-level edge-aware, class-activation map aware, and semantic tag-aware relations. Then, the discovered contrastive relations are unified into a universal objective for training the segmentation model, regardless of their heterogeneity. Moreover, we incorporate motion cues and unlabeled samples to increase the discriminative power and robustness of the segmentation model. Extensive experiments indicate that our proposed method produces reasonable segmentation.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment

DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-tra...

引用

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

作者： Yao, Lewei Han, Jianhua Liang, Xiaodan Xu, Dan Zhang, Wei Li, Zhenguo Xu, Hang Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image- text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo labeling process, DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. To accomplish this, we employ a maximum word-region similarity between region proposals and textual words to guide the contrastive objective. To enable the model to gain localization capability while learning broad concepts, DetCLIPv2 is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation. By jointly training with an alternating scheme and adopting low-resolution input for image-text pairs, DetCLIPv2 exploits image-text pair data efficiently and effectively: DetCLIPv2 utilizes 13x more image-text pairs than DetCLIP with a similar training time and improves performance. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance, e.g., DetCLIPv2 with Swin-T backbone achieves 40.4% zero-shot AP on the LVIS benchmark, which outperforms previous works GLIP/GLIPv2/DetCLIP by 14.4/11.4/4.5% AP, respectively, and even beats its fully-supervised counterpart by a large margin.

关键词： and reasoning language vision

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ShARc: Shape and Appearance recognition for Person Identification In-the-wild

ShARc: Shape and Appearance Recognition for Person Identific...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhu, Haidong Zheng, Wanrong Zheng, Zhaoheng Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9798350318920;9798350318937

Identifying individuals in unconstrained video settings is a valuable yet challenging task in biometric analysis due to variations in appearances, environments, degradations, and occlusions. In this paper, we present ShARc, a multimodal approach for video-based person identification in uncontrolled environments that emphasizes 3-D body shape, pose, and appearance. We introduce two encoders: a Pose and Shape Encoder (PSE) and an Aggregated Appearance Encoder (AAE). PSE encodes the body shape via binarized silhouettes, skeleton motions, and 3-D body shape, while AAE provides two levels of temporal appearance feature aggregation: attention-based feature aggregation and averaging aggregation. For attention-based feature aggregation, we employ spatial and temporal attention to focus on key areas for person distinction. For averaging aggregation, we introduce a novel flattening layer after averaging to extract more distinguishable information and reduce overfitting of attention. We utilize centroid feature averaging for gallery registration. We demonstrate significant improvements over existing state-of-the-art methods on public datasets, including CCVID, MEVID, and BRIAR.

关键词： Algorithms Algorithms Biometrics body pose face gesture Video recognition and understanding

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RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

RIDCP: Revitalizing Real Image Dehazing via High-Quality Cod...

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

作者： Wu, Rui-Qi Duan, Zheng-Peng Guo, Chun-Le Chai, Zhi Li, Chongyi Nankai Univ VCIP CS Tianjin Peoples R China Hisilicon Technol Co Ltd Shenzhen Guangdong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing. Code and data are released at https://***/projects/RIDCP.

关键词： Low-level vision

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Density-Based Flow Mask Integration via Deformable Convolution for Video People Flux Estimation

Density-Based Flow Mask Integration via Deformable Convoluti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wan, Chang-Lin Huang, Feng-Kai Shuai, Hong-Han Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798350318920;9798350318937

Crowd counting is currently applied in many areas, such as transportation hubs and streets. However, most of the research still focuses on counting the number of people in a single image, and there is little research on solving the problem of calculating the number of non-repeated people in a video segment. Currently, multiple object tracking is mainly relied upon for video counting, but this method is not suitable for situations where the crowd density is too high. Therefore, we propose a Flow Mask Integration Deformable Convolution network (FMDC) combined with Inter-Frame Head Contrastive Learning (IFHC) to predict the situation of people entering and exiting the screen in a density-based manner. We verify that our proposed method is highly effective in densely populated situations and diverse scenes, and the experimental results show that our proposed method surpasses existing methods.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures Video recognition and understanding

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Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation

Orthogonal Annotation Benefits Barely-supervised Medical Ima...

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

作者： Cai, Heng Li, Shumeng Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Natl Inst Healthcare Data Sci Nanjing Peoples R China Southest Univ Nanjing Peoples R China Shandong Womens Univ Jinan Peoples R China

ISBN: (纸本)9798350301298

Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Cotraining (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset. Our code and models are available at https:// github. com/ HengCai-NJU/DeSCO.

关键词： cell microscopy Medical and biological vision

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CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

CAT: LoCalization and IdentificAtion Cascade Detection Trans...

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

作者： Ma, Shuailei Wang, Yuefeng Wei, Ying Fan, Jiaqi Li, Thomas H. Liu, Hongli Lv, Fanbing Northeast Univ Shenyang Peoples R China Peking Univ Informat Technol R&D Innovat Ctr Shenzhen Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Changsha Hisense Intelligent Syst Res Inst Co Ltd Changsha Peoples R China

ISBN: (纸本)9798350301298

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Experiments on two benchmarks, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art methods. The code is publicly available at https://***/xiaomabufei/CAT.

关键词： detection recognition: Categorization retrieval

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The Background Also Matters: Background-Aware Motion-Guided Objects Discovery

The Background Also Matters: Background-Aware Motion-Guided ...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kara, Sandra Ammar, Hejer Chabot, Florian Quoc-Cuong Pham Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9798350318920;9798350318937

Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random segments. This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable. To address this limitation we propose BMOD, a Background-aware Motion-guided Objects Discovery method. Concretely, we leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground composed of both moving and static objects. The background, a complementary concept of the learned foreground class, is then isolated in the object discovery process. This enables a joint learning of the objects discovery task and the object/non-object separation. The conducted experiments on synthetic and real-world datasets show that integrating our background handling with various cutting-edge methods brings each time a considerable improvement. Specifically, we improve the objects discovery performance with a large margin, while establishing a strong baseline for object/non-object separation.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures Video recognition and understanding

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Semi-DETR: Semi-Supervised Object Detection with Detection Transformers

Semi-DETR: Semi-Supervised Object Detection with Detection T...

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

作者： Zhang, Jiacheng Lin, Xiangru Zhang, Wei Wang, Kuo Tan, Xiao Han, Junyu Ding, Errui Wang, Jingdong Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Sun Yat Sen Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency;(2) DETR-based detectors lack deterministic correspondence between the input query and its prediction output, which hinders the applicability of the consistency-based regularization widely used in current SSOD methods. We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector, to tackle these problems. Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage. Besides, we introduce a Cross-view Query Consistency method to learn the semantic feature invariance of object queries from different views while avoiding the need to find deterministic query correspondence. Furthermore, we propose a Cost-based Pseudo Label Mining module to dynamically mine more pseudo boxes based on the matching cost of pseudo ground truth bounding boxes for consistency training. Extensive experiments on all SSOD settings of both COCO and Pascal VOC benchmark datasets show that our Semi-DETR method outperforms all state-of-the-art methods by clear margins.

关键词： detection recognition: Categorization retrieval

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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

作者： Sun, Kexin Chen, Zhineng Wang, Gongwei Iu, Jun Ye, Xiongjun Gang, Yu -Gang Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Peking Univ Peoples Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Dept Urol Natl Canc Ctr Canc Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Canc Hosp Natl Clin Res Ctr Canc Beijing Peoples R China

ISBN: (纸本)9798350301298

The cost of pathological examination makes virtual re-staining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual re-staining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple post-processing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bi-directional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the interpatch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

关键词： cell microscopy Medical and biological vision

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