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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

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

作者： Krauss, Oren Kenyon-Dean, Kian Saberian, Saber Fallah, Maryam McLean, Peter Leung, Jess Sharma, Vasudev Khan, Ayla Balakrishnan, Jia Celik, Safiye Beaini, Dominique Sypetkowski, Maciej Cheng, Chi Vicky Morsel, Kristen Makes, Maureen Mabey, Ben Earnshaw, Berton Recurs Salt Lake City UT 84101 USA Valence Labs Rajpura India

ISBN: (纸本)9798350353006

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond. Relevant code and select models released with this work can be found at: https://***/recursionpharma/maes_microscopy.

关键词： cell biology cell morphology microscopy self-supervised learning SSL vision transformer ViT

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Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect vision-Language Pre-training Framework

Decomposing Disease Descriptions for Enhanced Pathology Dete...

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

作者： Vu Minh Hieu Phan Xie, Yutong Qi, Yuankai Liu, Linggiao Liu, Liyang Zhang, Bowen Liao, Zhibin Wu, Qi To, Minh-Son Verjans, Johan W. Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Macquarie Univ Sydney NSW Australia Flinders Univ S Australia Adelaide SA Australia

ISBN: (纸本)9798350353006

Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper, we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects, leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module, our approach aligns an input image with the diverse elements of a disease, generating aspect-centric image representations. By consolidating the matches from each aspect, we improve the compatibility between an image and its associated disease. Additionally, capitalizing on the aspect-oriented representations, we present a dual-head Transformer tailored to process known and unknown diseases, optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets, ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories, respectively. Our code is released at https://***/HieuPhan33/MAVL.

关键词： Medical vision-language pre-training vision-language pre-training Visual grounding Zero-shot classification

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Improving Image Restoration through Removing Degradations in Textual Representations

Improving Image Restoration through Removing Degradations in...

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

作者： Lin, Jingbo Zhang, Zhilu Wei, Yuxiang Ren, Dongwei Jiang, Dongsheng Tian, Qi Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Huawei Cloud Comp Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restoration is much easier on text modality than image one. For example, it can be easily conducted by removing degradation-related words while keeping the content-aware words. Hence, we combine the advantages of images in detail description and ones of text in degradation removal to perform restoration. To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration. In particular, We ingeniously embed an image-to-text mapper and text restoration module into CLIP-equipped text-to-image models to generate the guidance. Then, we adopt a simple coarse-to-fine approach to dynamically inject multi-scale information from guidance to image restoration networks. Extensive experiments are conducted on various image restoration tasks, including deblurring, dehazing, deraining, and denoising, and all-in-one image restoration. The results showcase that our method outperforms state-of-the-art ones across all these tasks. The codes and models are available at https://***/mrluin/TextualDegRemoval.

关键词： image restoration low-level vision

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

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

作者： Panzino, Andrea la Cava, Simone Maurizio Orru, Giulia Marcialis, Gian Luca Univ Cagliari Piazza Armi I-09123 Cagliari Italy

ISBN: (纸本)9798350365474

Due to the possibility of automatically verifying an individual's identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： detection face integration morphing

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Investigating Compositional Challenges in vision-Language Models for Visual Grounding

Investigating Compositional Challenges in Vision-Language Mo...

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

作者： Zeng, Yunan Huang, Yan Zhang, Jinjin Jie, Zequn Chai, Zhenhua Wang, Liang Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798350353006

Pre-trained vision-language models (VLMs) have achieved high performance on various downstream tasks, which have been widely used for visual grounding tasks in a weakly supervised manner. However, despite the performance gains contributed by large vision and language pre-training, we find that state-of-the-art VLMs struggle with compositional reasoning on grounding tasks. To demonstrate this, we propose Attribute, Relation, and Priority grounding (ARPGrounding) benchmark to test VLMs' compositional reasoning ability on visual grounding tasks. ARPGrounding contains 11,425 samples and evaluates the compositional understanding of VLMs in three dimensions: 1) attribute, denoting comprehension of objects' properties;2) relation, indicating an understanding of relation between objects;3) priority, reflecting an awareness of the part of speech associated with nouns. Using the ARPGrounding benchmark, we evaluate several mainstream VLMs. We empirically find that these models perform quite well on conventional visual grounding datasets, achieving performance comparable to or surpassing state-of-the-art methods but showing strong deficiencies in compositional reasoning. Furthermore, we propose a composition-aware fine- tuning pipeline, demonstrating the potential to leverage cost- effective image-text annotations for enhancing the compositional understanding of VLMs in grounding tasks. Code is available at link.

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EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

EfficientSAM: Leveraged Masked Image Pretraining for Efficie...

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

作者： Xiong, Yunyang Varadarajan, Bala Wu, Lemeng Xiang, Xiaoyu Xiao, Fanyi Zhu, Chenchen Dai, Xiaoliang Wang, Dilin Sun, Fei Iandola, Forrest Krishnamoorthi, Raghuraman Chandra, Vikas Meta AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353006

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial, the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation, we propose EfficientSAMs, lightweight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further, we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs, and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification, object detection, instance segmentation, and semantic segmentation, and find that our proposed pretraining method, SAMI, consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g., similar to 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at here.

关键词： EfficientSAM Masked Image Pretraining SAMI Segment Anything vision Transformer

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Scene Adaptive Sparse Transformer for Event-based Object Detection

Scene Adaptive Sparse Transformer for Event-based Object Det...

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

作者： Peng, Yansong Li, Hebei Zhang, Yueyi Sun, Xiaoyan Wu, Feng Univ Sci & Technol China Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350353006

While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks, their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However, they display inade-quate sparsity and adaptability when applied to event-based object detection, since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers, leading to reduced performance and efficiency. Furthermore, they lack scene-specific sparsity optimization, resulting in information loss and a lower recall rate. To overcome these limitations, we propose the Scene Adaptive Sparse Transformer ( SAST). SAST enables window-token co-sparsification, significantly enhancing fault tolerance and reducing computational overhead. Leveraging the innovative scoring and selection modules, along with the Masked Sparse Window Self-Attention, SAST showcases remarkable scene-aware adaptability: It focuses only on important objects and dynamically optimizes sparsity level according to scene complexity, maintaining a remarkable balance between performance and computational cost. The evaluation results show that SAST outperforms all other dense and sparse networks in both performance and efficiency on two large-scale event-based object detection datasets (1Mpx and Gen1). Code: https://***/Peterande/SAST.

关键词： Object recognition

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Robust Emotion recognition in Context Debiasing

Robust Emotion Recognition in Context Debiasing

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

作者： Yang, Dingkang Yang, Kun Li, Mingcheng Wang, Shunli Wang, Shuaibing Zhang, Lihua Fudan Univ Acad Engn & Technol Shanghai Peoples R China Cognit & Intelligent Technol Lab CIT Lab Beijing Peoples R China Jilin Prov Key Lab Intelligence Sci & Engn Changchun Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350353006

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.

关键词： Counterfactual inference Emotion recognition

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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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OVER-NAV: Elevating Iterative vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation

OVER-NAV: Elevating Iterative Vision-and-Language Navigation...

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

作者： Zhao, Ganlong Li, Guanbin Chen, Weikai Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Guangdong Peoples R China GuangDong Prov Key Lab Informat Secur Technol Guangzhou Guangdong Peoples R China Tencent Games Digital Content Technol Ctr Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350353006

Recent advances in Iterative vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task, it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end, we propose OVER-NAV, which aims to go over and beyond the current arts of IVLN techniques. In particular, we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data, we further introduce a structured representation, coded Omnigraph, to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism, OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition, OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.

关键词： Multi-Modal Learning Open-vocabulary vision-and-Language Navigation

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