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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

引用

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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Material Palette: Extraction of Materials from a Single Image

Material Palette: Extraction of Materials from a Single Imag...

引用

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

作者： Lopes, Ivan Pizzati, Fabio de Charette, Raoul INRIA Paris France Univ Oxford Oxford England

ISBN: (纸本)9798350353013;9798350353006

Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials, and finds extensive applications across computer graphics domains. However, acquiring PBR materials is costly and requires special apparatus. In this paper, we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concept tokens using a diffusion model, allowing the sampling of texture images resembling each material in the scene. Second, we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs), offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. Along with video, we share code and models as open-source on the project page: https://***/astra-vision/MaterialPalette

关键词： brdf computer graphics generative ai inversion Material estimation

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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...

引用

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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Gated Fields: Learning Scene Reconstruction from Gated Videos

Gated Fields: Learning Scene Reconstruction from Gated Video...

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

作者： Ramazzina, Andrea Walz, Stefanie Dahal, Pragyan Bijelic, Mario Heide, Felix Mercedes Benz Stuttgart Germany Saarland Univ Saarbrucken Germany Politecn Milan Milan Italy Torc Robot Blacksburg VA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes with scanning LiDAR sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps, we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end, we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our code and datasets are available here(1).

关键词： 3D vision Gated Camera Neural Radiance Fields Neural Rendering

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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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RoMa: Robust Dense Feature Matching

RoMa: Robust Dense Feature Matching

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

作者： Edstedt, Johan Sun, Qiyu Bokman, Georg Wadenback, Marten Felsberg, Michael Linkoping Univ Linkoping Sweden East China Univ Sci & Technol Shanghai Peoples R China Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350353006

Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences. The aim is to learn a robust model, i.e., a model able to match under challenging real-world changes. In this work, we propose such a model, leveraging frozen pretrained features from the foundation model DINOv2. Although these features are significantly more robust than local features trained from scratch, they are inherently coarse. We therefore combine them with specialized ConvNet fine features, creating a precisely localizable feature pyramid. To further improve robustness, we propose a tailored transformer match decoder that predicts anchor probabilities, which enables it to express multimodality. Finally, we propose an improved loss formulation through regression-by-classification with subsequent robust regression. We conduct a comprehensive set of experiments that show that our method, RoMa, achieves significant gains, setting a new state-of-the-art. In particular, we achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is provided at ***/Parskatt/RoMa.

关键词： 3D vision dense feature matching dense matching feature matching geometry estimation image matching two-view geometry

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GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting...

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

作者： Zheng, Shunyuan Zhou, Boyao Shao, Ruizhi Liu, Boning Zhang, Shengping Nie, Liqiang Liu, Yebin Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed. The code is available at https://***/aipixel/GPS-Gaussian.

关键词： Rendering (computer graphics)

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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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Referring Expression Counting

Referring Expression Counting

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

作者： Dai, Siyang Liu, Jun Cheung, Ngai-Man Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350353006

Existing counting tasks are limited to the class level, which don't account for fine-grained details within the class. In real applications, it often requires in-context or referring human input for counting target objects. Take urban analysis as an example, fine-grained information such as traffic flow in different directions, pedestrians and vehicles waiting or moving at different sides of the junction, is more beneficial. Current settings of both class-specific and class-agnostic counting treat objects of the same class indifferently, which pose limitations in real use cases. To this end, we propose a new task named Referring Expression Counting (REC) which aims to count objects with different attributes within the same class. To evaluate the REC task, we create a novel dataset named REC-8K which contains 8011 images and 17122 referring expressions. Experiments on REC-8K show that our proposed method achieves state-of-the-art performance compared with several text-based counting methods and an open-set object detection model. We also outperform prior models on the class agnostic counting (CAC) benchmark [36] for the zero-shot setting, and perform on par with the few-shot methods. Code and dataset is available at https://***/sydai/referring-expression-counting.

关键词： counting object detection referring expression vision-language model zero-shot

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Unseen And Adverse Outdoor Scenes recognition Through Event-based Captions

Unseen And Adverse Outdoor Scenes Recognition Through Event-...

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ieee/cvf International conference on computer vision (ICCV)

作者： Sakaino, Hidetomo Weathernews Inc Weather Transportat Lab Visual Recognit Grp Chiba Japan

ISBN: (纸本)9798350307443

This paper presents EventCAP, i.e., event-based captions, for refined and enriched qualitative and quantitative captions by Deep Learning (DL) models and vision Language Models (VLMs) with different tasks in a complementary manner. Indoor and outdoor images are used for object recognition and captioning. However, outdoor images in events change in wide ranges due to natural phenomena, i.e., weather changes. Such dynamical changes may degrade segmentation by illumination and object shape changes. This increases unseen objects and scenes under such adverse conditions. On the other hand, single state-of-art (SOTA) DLs and VLMs work with single or limited tasks, Therefore, this paper proposes EventCAP with captions with physical scales and objects' surface properties. Moreover, an iterative VQA model is proposed to refine incomplete segmented images with the prompts. A higher semantic level in captions for real-world scene descriptions is experimentally shown compared to SOTA VLMs.

关键词： adverse condition caption deep learning recognition segmentation vision language

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