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Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Weakly Supervised Set-Consistency Learning Improves Morpholo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Heming Yao Phil Hanslovsky Jan-Christian Huetter Burkhard Hoeckendorf David Richmond Biology Research | AI Development (BRAID) gCS Genentech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-content images remains an active area of research and is currently undergoing rapid innovation through the application of self-supervised learning and vision transformers. In this study, we propose a set-level consistency learning algorithm, Set-DINO, that combines self-supervised learning with weak supervision to improve learned representations of perturbation effects in single-cell images. Our method leverages the replicate structure of OPS experiments (i.e., cells undergoing the same genetic perturbation, both within and across batches) as a form of weak supervision. We conduct extensive experiments on a large-scale OPS dataset with more than 5000 genetic perturbations, and demonstrate that Set-DINO helps mitigate the impact of confounders and encodes more biologically meaningful information. In particular, Set-DINO recalls known biological relationships with higher accuracy compared to commonly used methods for morphological profiling, suggesting that it can generate more reliable insights from drug target discovery campaigns leveraging OPS.

关键词： computer vision Technological innovation Biomedical optical imaging Perturbation methods Self-supervised learning Optical imaging Genetics

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Taehoon Kim Mark Marsden Pyunghwan Ahn Sangyun Kim Sihaeng Lee Alessandra Sala Seung Hwan Kim LG AI Research Shutterstock

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： Training Measurement Accuracy conferences computer architecture Feature extraction Data models

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Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Riu Cherdchusakulchai Sasin Phimsiri Visarut Trairattanapa Suchat Tungjitnob Wasu Kudisthalert Pornprom Kiawjak Ek Thamwiwatthana Phawat Borisuitsawat Teepakorn Tosawadi Pakcheera Choppradit Kasisdis Mahakijdechachai Supawit Vatathanavaro Worawit Saetan Vasin Suttichaya AI and Robotics Ventures (ARV) Thailand

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024Track1ARV

关键词： Urban areas Pipelines Cameras Feature extraction Real-time systems pattern recognition Synchronization

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Blind Localization and Clustering of Anomalies in Textures

Blind Localization and Clustering of Anomalies in Textures

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrei-Timotei Ardelean Tim Weyrich Friedrich-Alexander-Universität Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is anomaly clustering, i.e., identifying and grouping different types of anomalies in a fully unsupervised manner. In this work, we propose a novel method for clustering anomalies in largely stationary images (textures) in a blind setting. That is, the input consists of normal and anomalous images without distinction and without labels. What contributes to the difficulty of the task is that anomalous regions are often small and may present only subtle changes in appearance, which can be easily overshadowed by the genuine variance in the texture. Moreover, each anomaly type may have a complex appearance distribution. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning. By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation. Our experiments show that the proposed solution yields significantly better results compared to prior work, setting a new state of the art. Project page: ***/pub/***.

关键词： Location awareness computer vision Image texture Art conferences Pipelines Contrastive learning

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Video-based Person Re-identification without Bells and Whistles

Video-based Person Re-identification without Bells and Whist...

引用

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

作者： Liu, Chih-Ting Chen, Jun-Cheng Chen, Chu-Song Chien, Shao-Yi Natl Taiwan Univ Grad Inst Elect & Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical Nonlocal Network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91:3% in rank-1 and 86:5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://***/jackie840129/CF-AAN.

关键词： computer vision Protocols Computational modeling conferences Lighting Cameras Data models

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yi-Shan Lee Wei-Cheng Tseng Fu-En Wang Min Sun National Tsing Hua University University of Toronto Vector Institute

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer vision conferences Music Recommender systems pattern matching

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eliahu Horwitz Yedid Hoshen School of Computer Science and Engineering The Hebrew University of Jerusalem Israel

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ruggero Ragonesi Pietro Morerio Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) - Istituto Italiano di Tecnologia Department of Computer Science University of Verona

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ziyue Xiang Amit Kumar Singh Yadav Paolo Bestagini Stefano Tubaro Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods.

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