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Hierarchical discriminative learning improves visual representations of biomedical microscopy

Hierarchical discriminative learning improves visual represe...

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

作者： Jiang, Cheng Hou, Xinhai Kondepudi, Akhil Chowdury, Asadur Freudiger, Christian W. Orringer, Daniel A. Lee, Honglak Hollon, Todd C. Univ Michigan Ann Arbor MI 48109 USA Invenio Imaging Santa Clara CA USA NYU New York NY USA LG AI Res Seoul South Korea

ISBN: (纸本)9798350301298

Learning high-quality, self-supervised, visual representations is essential to advance the role of computer vision in biomedical microscopy and clinical medicine. Previous work has focused on self-supervised representation learning (SSL) methods developed for instance discrimination and applied them directly to image patches, or fields-of-view, sampled from gigapixel whole-slide images (WSIs) used for cancer diagnosis. However, this strategy is limited because it (1) assumes patches from the same patient are independent, (2) neglects the patient-slide-patch hierarchy of clinical biomedical microscopy, and (3) requires strong data augmentations that can degrade downstream performance. Importantly, sampled patches from WSIs of a patient's tumor are a diverse set of image examples that capture the same underlying cancer diagnosis. This motivated HiDisc, a data-driven method that leverages the inherent patient-slide-patch hierarchy of clinical biomedical microscopy to define a hierarchical discriminative learning task that implicitly learns features of the underlying diagnosis. HiDisc uses a self-supervised contrastive learning framework in which positive patch pairs are defined based on a common ancestry in the data hierarchy, and a unified patch, slide, and patient discriminative learning objective is used for visual SSL. We benchmark HiDisc visual representations on two vision tasks using two biomedical microscopy datasets, and demonstrate that (1) HiDisc pretraining outperforms current state-of-the-art self-supervised pretraining methods for cancer diagnosis and genetic mutation prediction, and (2) HiDisc learns high-quality visual representations using natural patch diversity without strong data augmentations. [GRAPHICS] .

关键词： cell microscopy Medical and biological vision

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C²T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute recognition

C<SUP>2</SUP>T-Net: Channel-Aware Cross-Fused Transformer-St...

引用

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

作者： Bui, Doanh C. Thinh V. Le Ngo, Ba Hung Korea Univ Sch Elect Engn Seoul South Korea Vietnam Natl Univ Univ Informat Technol Ho Chi Minh City Vietnam Chonnam Natl Univ Grad Sch Data Sci Gwangju South Korea

ISBN: (纸本)9798350370287;9798350370713

Pedestrian attribute recognition (PAR) poses a significant challenge but holds practical significance in various security applications, including surveillance. In the scope of the UPAR challenge, this paper introduces the Channel-Aware Cross-Fused Transformer-Style Networks ((CT)-T-2-Net). This network effectively integrates two powerful transformer-style networks, namely the Swin Transformer (SwinT) and a customized variant of the vanilla vision transformer (EVA ViT). The aim is to capture both local and global aspects of an individual for precise attribute recognition. To facilitate the understanding of intricate relationships among channels, a channel-aware self-attention mechanism is devised and integrated into each SwinT block. Furthermore, the fusion of features from the two transformer-style networks is accomplished through cross-fusion, enabling each network to mutually amplify and boost the textural nuances present in the other. The efficacy of the proposed model has been demonstrated through its performance on three PAR benchmarks: PA100K, PETA, and the UPAR2024 private test. With respect to the PA100K benchmark, our approach has achieved state-of-the-art results when compared to models that do not employ any pre-training techniques. Our performance on the PETA dataset remains competitive, standing on par with other cutting-edge models. Notably, our model achieved runner-up performance on the UPAR2024-track-1 test set. Source code is available at https://***/caodoanh2001/upar_challenge.

关键词： computer vision

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Segment anything, from space?

Segment anything, from space?

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

作者： Ren, Simiao Luzi, Francesco Lahrichi, Saad Kassaw, Kaleb Collins, Leslie M. Bradbury, Kyle Make, Jordan M. Duke Univ Elect & Comp Engn Durham NC 27708 USA Duke Kunshan Univ Div Nat & Appl Sci Suzhou Peoples R China Duke Univ Nicholas Inst Energy Environm & Sustainabil Durham NC USA Univ Montana Comp Sci Missoula MT 59812 USA

ISBN: (纸本)9798350318920;9798350318937

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the zero-shot image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery. In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development. We examine SAM's performance on a set of diverse and widely studied benchmark tasks. We find that SAM does often generalize well to overhead imagery, although it fails in some cases due to the unique characteristics of overhead imagery and its common target objects. We report on these unique systematic failure cases for remote sensing imagery that may comprise useful future research for the community.

关键词： Algorithms Applications Image recognition and understanding Remote Sensing

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Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark

Amodal Intra-class Instance Segmentation: Synthetic Datasets...

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

作者： Ao, Jiayang Ke, Qiuhong Ehinger, Krista A. Univ Melbourne Parkville Vic 3010 Australia Monash Univ Clayton Vic 3800 Australia

ISBN: (纸本)9798350318920;9798350318937

Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging. Although important for downstream tasks such as robotic grasping systems, the lack of large-scale amodal datasets with detailed annotations makes it difficult to model intra-class occlusions explicitly. This paper introduces two new amodal datasets for image amodal completion tasks, which contain a total of over 267K images of intra-class occlusion scenarios, annotated with multiple masks, amodal bounding boxes, dual order relations and full appearance for instances and background. We also present a point-supervised scheme with layer priors for amodal instance segmentation specifically designed for intra-class occlusion scenarios(1). Experiments show that our weakly supervised approach outperforms the SOTA fully supervised methods, while our layer priors design exhibits remarkable performance improvements in the case of intra-class occlusion in both synthetic and real images.

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

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Unsupervised 3D Skeleton-Based Action recognition using Cross-Attention with Conditioned Generation Capabilities

Unsupervised 3D Skeleton-Based Action Recognition using Cros...

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

作者： Lerch, David J. Zhong, Zeyun Martin, Manuel Voit, Michael Beyerer, Juergen Fraunhofer IOSB Karlsruhe Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350370287;9798350370713

Human action recognition plays a pivotal role in various real-world applications, including surveillance systems, robotics, and occupant monitoring in the car interior. With such a diverse range of domains, the demand for generalization becomes increasingly crucial. In this work, we propose a cross-attention-based encoder-decoder approach for unsupervised 3D skeleton-based action recognition. Specifically, our model takes a skeleton sequence as input for the encoder and further applies masking and noise to the original sequence for the decoder. By training the model to reconstruct the original skeleton sequence, it simultaneously learns to capture the underlying patterns of actions. Extensive experiments on NTU and NW-UCLA datasets demonstrate the state-of-the-art performance as well as the impressive generalizability of our proposed approach. Moreover, our experiments reveal that our approach is capable of generating conditioned skeleton sequences, offering the potential to enhance small datasets or generate samples of under-represented classes in imbalanced datasets. Our code will be published on GitHub.

关键词： Musculoskeletal system

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FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation

FAKD: Feature Augmented Knowledge Distillation for Semantic ...

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

作者： Yuan, Jianlong Phan, Minh Hieu Liu, Liyang Liu, Yifan Alibaba Grp Damo Acad Hangzhou Peoples R China Hupan Lab Hangzhou Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350318920;9798350318937

In this work, we explore data augmentations for knowledge distillation on semantic segmentation. Due the capacity gap, small-sized student networks struggle to discover the discriminative feature space learned by a powerful teacher. Image-level augmentations allow the student to better imitate the teacher by providing extra outputs. However, existing distillation frameworks only augment a limited number of samples, which restricts the learning of a student. Inspired by the recent progress on semantic directions on feature space, this work proposes a feature-level augmented knowledge distillation (FAKD) which infinitely augments features along a semantic direction for optimal knowledge transfer. Furthermore, we introduce novel surrogate loss functions to distill the teacher's knowledge from an infinite number of samples. The surrogate loss is an upper bound of the expected distillation loss over infinite augmented samples. Extensive experiments on four semantic segmentation benchmarks demonstrate that the proposed method boosts the performance of current knowledge distillation methods without any significant overhead. The code will be released at FAKD.

关键词： Algorithms Image recognition and understanding

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Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation

Video-kMaX: A Simple Unified Approach for Online and Near-On...

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

作者： Shin, Inkyu Kim, Dahun Yu, Qihang Xie, Jun Kim, Hong-Seok Green, Bradley Kweon, In So Yoon, Kuk-Jin Chen, Liang-Chieh Korea Adv Inst Sci & Technol Daejeon South Korea Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350318920;9798350318937

Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can be categorized into online and near-online approaches. Evolving over the time, each category has its own specialized designs, making it nontrivial to adapt models between different categories. To alleviate the discrepancy, in this work, we propose a unified approach for online and near-online VPS. The meta architecture of the proposed Video-kMaX consists of two components: within-clip segmenter (for clip-level segmentation) and cross-clip associater (for association beyond clips). We propose clip-kMaX (clip k-means mask transformer) and LA-MB (location-aware memory buffer) to instantiate the segmenter and associater, respectively. Our general formulation includes the online scenario as a special case by adopting clip length of one. Without bells and whistles, Video-kMaX sets a new state-of-the-art on KITTI-STEP and VIPSeg for video panoptic segmentation Code and models are available at this link.

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

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Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video recognition

Automated Sperm Assessment Framework and Neural Network Spec...

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

作者： Fujii, Takuro Nakagawa, Hayato Takeshima, Teppei Yumura, Yasushi Hamagami, Tomoki Yokohama Natl Univ Yokohama Kanagawa Japan Yokohama City Univ Med Ctr Yokohama Kanagawa Japan

ISBN: (纸本)9798350318920;9798350318937

Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck). Our code is publickly available at https://***/FTKR12/RoSTFine.

关键词： Animals / Insects Applications Applications Biomedical / healthcare / medicine

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dacl10k: Benchmark for Semantic Bridge Damage Segmentation

dacl10k: Benchmark for Semantic Bridge Damage Segmentation

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

作者： Flotzinger, Johannes Roesch, Philipp J. Braml, Thomas Univ Bundeswehr Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Reliably identifying reinforced concrete defects (RCDs) plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.

关键词： Algorithms Algorithms Applications Datasets and evaluations Image recognition and understanding Structural engineering / civil engineering

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Adaptive Zone-aware Hierarchical Planner for vision-Language Navigation

Adaptive Zone-aware Hierarchical Planner for Vision-Language...

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

作者： Gao, Chen Peng, Xingyu Yan, Mi Wang, He Yang, Lirong Ren, Haibing Li, Hongsheng Liu, Si Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Meituan Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

The task of vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of subgoals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and subgoal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R).

关键词： language reasoning vision

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