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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

引用

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

作者： Ahousamra, Shahira Gupta, Rajarsi Kurc, Tahsin Samaras, Dimitris Saltz, Joel Chen, Chao SUNY Stony Brook Dept Comp Sci Stony Brook NY 11790 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY USA

ISBN: (纸本)9798350301298

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

关键词： cell microscopy Medical and biological vision

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Uni-NLX: Unifying Textual Explanations for vision and vision-Language Tasks

Uni-NLX: Unifying Textual Explanations for Vision and Vision...

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ieee/CVF International conference on computer vision (ICCV)

作者： Sammani, Fawaz Deligiannis, Nikos Vrije Univ Brussel ETRO Dept Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9798350307443

Natural Language Explanations (NLE) aim at supplementing the prediction of a model with human-friendly natural text. Existing NLE approaches involve training separate models for each downstream task. In this work, we propose Uni-NLX, a unified framework that consolidates all NLE tasks into a single and compact multi-task model using a unified training objective of text generation. Additionally, we introduce two new NLE datasets: 1) ImageNetX, a dataset of 144K samples for explaining ImageNet categories, and 2) VQA-ParaX, a dataset of 123K samples for explaining the task of Visual Question Answering (VQA). Both datasets are derived leveraging large language models (LLMs). By training on the 1M combined NLE samples, our single unified framework is capable of simultaneously performing seven NLE tasks including VQA, visual recognition and visual reasoning tasks with 7xfewer parameters, demonstrating comparable performance to the independent task-specific models in previous approaches, and in certain tasks even outperforming them.(1)

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Benchmarking Self-Supervised Learning on Diverse Pathology D...

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

作者： Kang, Mingu Song, Heon Park, Seonwook Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning (SSL) has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements. We release the pre-trained model weights(1).

关键词： cell microscopy Medical and biological vision

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

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

作者： Zhang, Zhicheng Wang, Lijuan Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

关键词： vision applications and systems

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Directional Connectivity-based Segmentation of Medical Images

Directional Connectivity-based Segmentation of Medical Image...

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

作者： Yang, Ziyun Farsiu, Sina Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798350301298

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://***/Zyun-Y/DconnNet.

关键词： cell microscopy Medical and biological vision

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DRT: A Lightweight Single Image Deraining Recursive Transformer

DRT: A Lightweight Single Image Deraining Recursive Transfor...

引用

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

作者： Liang, Yuanchu Anwar, Saeed Liu, Yang Australian Natl Univ Canberra Australia CSIRO Data61 Sydney Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task;nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only similar to 1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://***/YC-Liang/DRT.

关键词： Training Deep learning computer vision Rain Computational modeling computer architecture Transformers

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Event Transformer. A sparse-aware solution for efficient event data processing

Event Transformer. A sparse-aware solution for efficient eve...

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

作者： Sabater, Alberto Montesano, Luis Murillo, Ana C. Univ Zaragoza DIIS I3A Zaragoza Spain Bitbrain Technol Zaragoza Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Event cameras are sensors of great interest for many applications that run in low-resource and challenging environments. They log sparse illumination changes with high temporal resolution and high dynamic range, while they present minimal power consumption. However, top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms. Efforts toward efficient solutions usually do not achieve top-accuracy results for complex tasks. This work proposes a novel framework, Event Transformer (EvT)(1), that effectively takes advantage of event-data properties to be highly efficient and accurate. We introduce a new patch-based event representation and a compact transformer-like architecture to process it. EvT is evaluated on different event-based benchmarks for action and gesture recognition. Evaluation results show better or comparable accuracy to the state-of-the-art while requiring significantly less computation resources, which makes EvT able to work with minimal latency both on GPU and CPU.

关键词： Power demand Graphics processing units Gesture recognition computer architecture Benchmark testing Transformers Data processing

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A New Dataset and Transformer for Stereoscopic Video Super-Resolution

A New Dataset and Transformer for Stereoscopic Video Super-R...

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

作者： Imani, Hassan Islam, Md Baharul Wong, Lai-Kuan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video. The key challenges in SVSR are preserving the stereo-consistency and temporal-consistency, without which viewers may experience 3D fatigue. There are several notable works on stereoscopic image super-resolution, but there is little research on stereo video super-resolution. In this paper, we propose a novel Transformer-based model for SVSR, namely Trans-SVSR. Trans-SVSR comprises two key novel components: a spatio-temporal convolutional self-attention layer and an optical flow-based feed-forward layer that discovers the correlation across different video frames and aligns the features. The parallax attention mechanism (PAM) that uses the cross-view information to consider the significant disparities is used to fuse the stereo views. Due to the lack of a benchmark dataset suitable for the SVSR task, we collected a new stereoscopic video dataset, SVSR-Set, containing 71 full high-definition (HD) stereo videos captured using a professional stereo camera. Extensive experiments on the collected dataset, along with two other datasets, demonstrate that the Trans-SVSR can achieve competitive performance compared to the state-of-the-art methods. Project code and additional results are available at https://***/H-deep/Trans-SVSR/.

关键词： Three-dimensional displays Stereo image processing Superresolution Transformers Optical imaging pattern recognition Task analysis

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CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

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

作者： Guirguis, Karim Hendawy, Ahmed Eskandar, George Abdelsamad, Mohamed Kayser, Matthias Beyerer, Juergen Robert Bosch GmbH Stuttgart Germany Univ Stuttgart Stuttgart Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9781665487399

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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TikTok for good: Creating a diverse emotion expression database

TikTok for good: Creating a diverse emotion expression datab...

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

作者： Surabhi, Saimourya Shah, Bhavik Washington, Peter Mutlu, Onur Cezmi Leblanc, Emilie Mohite, Prathamesh Husic, Arman Kline, Aaron Dunlap, Kaitlyn McNealis, Maya Liu, Bennett Deveaux, Nick Sleiman, Essam Wall, Dennis P. Stanford Univ Dept Biomed Data Sci Dept Pediat Syst Med Stanford CA 94305 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition (FER) is a critical computer vision task for a variety of applications. Despite the widespread use of FER, there is a dearth of racially diverse facial emotion datasets which are enriched for children, teens, and adults. To bridge this gap, we have built a diverse expression recognition database using publicly available videos from TikTok, a video-focused social networking service. We describe the construction of the TikTok Facial expression recognition (FER) database. The dataset is extracted from 6428 videos scraped from TikTok. The videos consist of 9392 distinct individuals and labels for 15 emotion-related prompts. We were able to achieve a F1 score 0.78 for Ekman emotions on expression classification using transfer learning. We hope that the scale and diversity of the TikTokFER dataset will be of use to affective computing practitioners.

关键词： Bridges computer vision Emotion recognition Databases Social networking (online) Face recognition conferences

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