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CPLIP: Zero-Shot Learning for Histopathology with Comprehensive vision-Language Alignment

CPLIP: Zero-Shot Learning for Histopathology with Comprehens...

引用

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

作者： Javed, Sajid Mahmood, Arif Ganapathil, Iyyakutti Iyappan Dharej, Fayaz Ali Werghil, Naoufel Bennamoun, Mohammed Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol C2PS Abu Dhabi U Arab Emirates Informat Technol Univ Punjab Lahore Pakistan Univ Western Australia Perth WA Australia

ISBN: (纸本)9798350353006

This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks, CPLIP shows notable improvements in zero-shot learning scenarios, outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication, the code for CPLIP is available on GitHub at https://***/

关键词： Cancer Detection Computational Pathology Contrastive Loss Histopathology Many-to-Many vision-Language Alignment vision Language Modeling Whole Slide Image Zero-shot Learning

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ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

ICON: Incremental CONfidence for Joint Pose and Radiance Fie...

引用

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

作者： Wang, Weiyao Gleize, Pierre Tang, Hao Chen, Xingyu Liang, Kevin J. Feiszli, Matt Meta FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.

关键词： Three dimensional computer graphics

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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

引用

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

作者： Kim, Taehoon Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Marsden, Mark Sala, Alessandra Kim, Seung Hwan Han, Bohyung Lee, Kyoung Mu Lee, Honglak Bae, Kyounghoon Wu, Xiangyu Gao, Yi Zhang, Hailiang Yang, Yang Guo, Weili Lu, Jianfeng Oh, Youngtaek Cho, Jae Won Kim, Dong-Jin Kweon, In So Kim, Junmo Kang, Wooyoung Jhoo, Won Young Roh, Byungseok Mun, Jonghwan Oh, Solgil Ak, Kenan Emir Lee, Gwang-Gook Xu, Yan Shen, Mingwei Hwang, Kyomin Shin, Wonsik Lee, Kamin Park, Wonhark Lee, Dongkwan Kwak, Nojun Wang, Yujin Wang, Yimu Gu, Tiancheng Lv, Xingchang Sun, Mingmao

ISBN: (纸本)9798350365474

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Image captioning Multimodal representation vision-language models

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BigGait: Learning Gait Representation You Want by Large vision Models

BigGait: Learning Gait Representation You Want by Large Visi...

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

作者： Ye, Dingqiang Fan, Chao Ma, Jingzhe Liu, Xiaoming Yu, Shiqi Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353013;9798350353006

Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences, which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations, effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Finally, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code is available at https://github. com/ShiqiYu/OpenGait.

关键词： Gait recognition Large vision Models Person Re-Identification

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Cross-view and Cross-pose Completion for 3D Human Understanding

Cross-view and Cross-pose Completion for 3D Human Understand...

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

作者： Armando, Matthieu Galaaoui, Salma Baradel, Fabien Lucas, Thomas Leroy, Vincent Bregier, Romain Weinzaepfel, Philippe Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353013;9798350353006

Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose, object-centric image datasets such as ImageNet, is limited by an important domain shift. On the other hand, collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore, we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs, and temporal (cross-pose) pairs taken from videos, in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture, these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks, and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.

关键词： hand-centric human-centric pretraining representation learning vision tranformer

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Boosting Continual Learning of vision-Language Models via Mixture-of-Experts Adapters

Boosting Continual Learning of Vision-Language Models via Mi...

引用

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

作者： Yu, Jiazuo Zhuge, Yunzhi Zhang, Lu Hu, Ping Wang, Dong Lu, Huchuan He, You Dalian Univ Technol Dalian Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353006

Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout life-long learning and (ii) significant computational burdens associated with full-model tuning. In this work, we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models, we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively. Through extensive experiments across various settings, our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%. Our code locates at https://***/JiazuoYu/MoE-Adapters4CL

关键词： class incremental learning continual learning task incremental learning vision-Language Model

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AM-RADIO: Agglomerative vision Foundation Model Reduce All Domains Into One

AM-RADIO: Agglomerative Vision Foundation Model Reduce All D...

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

作者： Ranzinger, Mike Heinrich, Greg Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9798350353006

A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences, these models can be effectively merged into a unified model through multi-teacher distillation. We name this approach AM-RADIO (Agglomerative Model - Reduce All Domains Into One). This integrative approach not only surpasses the performance of individual teacher models but also amalgamates their distinctive features, such as zero-shot vision-language comprehension, detailed pixel-level understanding, and open vocabulary segmentation capabilities. Additionally, in pursuit of the most hardware-efficient backbone, we evaluated numerous architectures in our multi-teacher distillation pipeline using the same training recipe. This led to the development of a novel architecture (E-RADIO) that exceeds the performance of its predecessors and is at least 6x faster than the teacher models at matched resolution. Our comprehensive benchmarking process covers downstream tasks including ImageNet classification, semantic segmentation linear probing, COCO object detection and integration into LLaVa-1.5. Code: https://***/NVlabs/RADIO.

关键词： Distillation Knowledge Distillation Semantic Segmentation Visual Foundation Model Visual Large Language Models Zero Shot Classification

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Flow-Guided Online Stereo Rectification for Wide Baseline Stereo

Flow-Guided Online Stereo Rectification for Wide Baseline St...

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

作者： Kumar, Anush Mannan, Fahim Jafari, Omid Hosseini Li, Shile Heider, Felix Torc Robot Blacksburg VA 24060 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However, autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors, including vibration, and structural stress, when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles, such as autonomous freight trucks and semi-trucks, the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges, we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint, and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration, and substantially improves downstream stereo depth, post-rectification. We release two public datasets (https://***/online-stereo-recification/), a synthetic and experimental wide baseline dataset, to foster further research.

关键词： Autonomous Driving Camera Pose Estimation computer vision Rectification Datasets Stereo Rectification Wide Baseline Stereo

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From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation

From Correspondences to Pose: Non-minimal Certifiably Optima...

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

作者： Tirado-Garin, Javier Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9798350353013;9798350353006

Estimating the relative camera pose from n >= 5 correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views, and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper, we demonstrate a novel approach that, for the first time, bypasses the second stage. Specifically, we show that it is possible to directly estimate the correct relative camera pose from correspondences without needing a post-processing step to enforce the cheirality constraint on the correspondences. Building on recent advances in certifiable non-minimal optimization, we frame the relative pose estimation as a Quadratically Constrained Quadratic Program (QCQP). By applying the appropriate constraints, we ensure the estimation of a camera pose that corresponds to a valid 3D geometry and that is globally optimal when certified. We validate our method through exhaustive synthetic and real-world experiments, confirming the efficacy, efficiency and accuracy of the proposed approach. Code is available at https://***/javrtg/C2P.

关键词： computer vision convex optimization epipolar geometry essential matrix non-minimal solver relative pose semidefinite programming

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

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

作者： Prabhune, Omkar Chen, Tianen Kim, Younghyun Purdue Univ W Lafayette IN 47907 USA Univ Wisconsin Madison WI 53706 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350365474

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication overhead. In this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a contentaware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： AI on edge computation offloading content-aware compression content-aware offloading deep learning edge computing edge ML efficient deep learning efficient ML embedded vision face identification low-latency deep learning low-power ML ml system tinyML

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