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ViLa-MIL: Dual-scale vision-Language Multiple Instance Learning for Whole Slide Image Classification

ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learn...

引用

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

作者： Shi, Jiangbo Li, Chen Gong, Tieliang Zheng, Yefeng Fu, Huazhu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Jarvis Res Ctr Tencent YouTu Lab Shenzhen Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350353006

Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However, these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides, which are easily affected by variations in data distribution. Recently, vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However, the previous text prompt lacks the consideration of pathological prior knowledge, therefore does not substantially boost the model's performance. Moreover, the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems, we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically, we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to pro-cess WSI efficiently, for the image branch, we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype;for the text branch, we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL.

关键词： Histopathology Multiple Instance Learning vision-Language Models Whole Slide Image Classification

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BlockGCN: Redefine Topology Awareness for Skeleton-Based Action recognition

BlockGCN: Redefine Topology Awareness for Skeleton-Based Act...

引用

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

作者： Zhou, Yuxuan Yan, Xudong Cheng, Zhi-Qi Yan, Yan Dai, Qi Hua, Xian-Sheng Univ Mannheim Mannheim Germany City Univ Macau Taipa Macao Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci SIAT Beijing Peoples R China Microsoft Res Redmond WA USA Terminus Grp Hyderabad India

ISBN: (纸本)9798350353013;9798350353006

Graph Convolutional Networks (GCNs) have long set the state-of- the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However, an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process, while seemingly efficient, causes a gradual decay of bone connectiv-ity data, resulting in a model indifferent to the very topology it sought to represent. To remedy this, we propose a two-fold strategy: (1) We introduce an innovative approach that encodes bone connectivity by harnessing the power of graph distances to describe the physical topology;we further incorporate action-specific topological representation via persistent homology analysis to depict systemic dynamics. This preserves the vital topological nuances often lost in conventional GCNs. (2) Our investigation also reveals the redundancy in existing GCNs for multi-relational modeling, which we address by proposing an efficient refinement to Graph Convolutions ( GC) - the BlockGC. This significantly reduces parameters while improving performance beyond original GCNs. Our full model, BlockGCN, establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design, most notably on the large-scale NTU RGB+D 120 dataset, stand as strong validation of the efficacy of BlockGCN.

关键词： Benchmarking

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Visual Programming: Compositional visual reasoning without training

Visual Programming: Compositional visual reasoning without t...

引用

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

作者： Gupta, Tanmay Kembhavi, Aniruddha PRIOR Allen Inst AI Seattle WA 98103 USA

ISBN: (纸本)9798350301298

We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing subroutines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.

关键词： and reasoning language vision

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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art ...

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

作者： Wang, Chien-Yao Bochkovskiy, Alexey Liao, Hong-Yuan Mark Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9798350301298

Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. Source code is released in https://***/WongKinYiu/yolov7.

关键词： detection recognition: Categorization retrieval

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Towards Explainable Visual Vessel recognition Using Fine-Grained Classification and Image Retrieval

Towards Explainable Visual Vessel Recognition Using Fine-Gra...

引用

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

作者： Karus, Heiko Schwenker, Friedhelm Munz, Michael Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Ulm Univ Ulm Germany Ulm Univ Appl Sci Ulm Germany

ISBN: (纸本)9798350365474

The precise recognition of vessel types is critical for applications in maritime surveillance, but manual visual inspection is slow and error-prone. Automated fine-grained object recognition helps to quickly and accurately categorize vessels, as long as they overcome challenges of modern deep and machine learning-based methods such as class imbalance or intransparency. This paper utilizes recent literature to work towards this task considering two different problem formulations: fine-grained classification and image retrieval. We create a novel dataset called Military MARVEL consisting of 15,858 images of 137 military vessel classes to conduct the extensive, survey-like experiments. On two maritime datasets, we demonstrate that end-to-end fine-grained classification leads to slightly higher accuracy for the price of less flexibility compared to image retrieval. Heatmap-based eXplainable Artificial Intelligence methods are combined with the vessel recognition approaches to assess the achieved transparency quantitatively and qualitatively. Notably, quantitative measures are consistent with qualitative evaluation, especially in fine-grained classification. Our Military MARVEL dataset is available at https://***/HensoldtOptronicsCV/FineGrainedVesselrecognition.

关键词： Military photography

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Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture

Frequency Decoupling for Motion Magnification via Multi-Leve...

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

作者： Wang, Fei Guo, Dan Li, Kun Zhong, Zhun Wang, Meng Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9798350353006

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observe that the low-frequency components with stable energy always possess spatial structure and less noise, making them suitable for modeling the subtle motion field. To this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degradation due to their inherent subtlety and unavoidable external interference from noise, we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures, and a Sparse Frequency Mixer to promote seamless recoupling. Besides, we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features, reducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63x and boosts inference speed by 1.68x than the latest method. Our code is available at https://***/Jiafei127/FD4MM.

关键词： computer vision Frequency Decoupling Video Motion Magnification

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

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

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

作者： Kim, Taehoon Marsden, Mark Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Sala, Alessandra Kim, Seung Hwan LG AI Res Seoul South Korea Shutterstock New York NY USA

ISBN: (纸本)9798350365474

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： image captioning large-scale multimodal transformers vision-language zero-shot

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Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

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

作者： Hu, Yueyu Gong, Ran Sun, Qi Wang, Yao NYU Tandon Sch Engn Brooklyn NY 11201 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350365474

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Rendering (computer graphics)

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PartDistill: 3D Shape Part Segmentation by vision-Language Model Distillation

PartDistill: 3D Shape Part Segmentation by Vision-Language M...

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

作者： Umam, Ardian Yang, Cheng-Kun Chen, Min-Hung Chuang, Jen-Hui Lin, Yen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan MediaTek Hsinchu Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353013;9798350353006

This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections, inconsistent 2D predictions by VLMs, and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bidirectional distillation, including forward and backward distillations, is carried out within the framework, where the former forward distills the 2D predictions to the student network, and the latter improves the quality of the 2D predictions, which subsequently enhances the final 3D segmentation. Moreover, PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments, PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartNetE datasets, by more than 15% and 12% higher mIoU scores, respectively. The code for this work is available at https://***/ardianumam/PartDistill.

关键词： 3d part segmentation cross-modal distillation zero-shot segmentation

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Scaling Language-Image Pre-training via Masking

Scaling Language-Image Pre-training via Masking

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

作者： Li, Yanghao Fan, Haoqi Hu, Ronghang Feichtenhofert, Christoph He, Kaiming Meta AI FAIR New York NY 10023 USA

ISBN: (纸本)9798350301298

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP [52]. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning.

关键词： and reasoning language vision

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