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Scaling Graph Convolutions for Mobile vision

Scaling Graph Convolutions for Mobile Vision

引用

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

作者： Avery, William Munir, Mustafa Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

To compete with existing mobile architectures, MobileViG introduces Sparse vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, Mobile-ViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 AP(box) and 0.7 AP(mask), and MobileViGv2-B outperforms MobileViG-B by 1.0 AP(box) and 0.7 APmask. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU (1).

关键词： computer vision Deep Learning Edge AI Graph Neural Networks

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Building vision-Language Models on Solid Foundations with Masked Distillation

Building Vision-Language Models on Solid Foundations with Ma...

引用

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

作者： Sameni, Sepehr Kafle, Kushal Tan, Hao Jenni, Simon Univ Bern Bern Switzerland Adobe Res San Jose CA USA

ISBN: (纸本)9798350353006

Recent advancements in vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However, traditional VLMs, trained through contrastive learning on limited and noisy image-text pairs, often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach, Solid Foundation CLIP (SF-CLIP), circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result, SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities, setting a new state of the art for ViT-B/16 trained on YFCC15M and CC12M. Moreover, the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency, evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.

关键词： CLIP Distillation LLM Multilingual Multimodal Representation Learning

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Learning by Correction: Efficient Tuning Task for Zero-Shot Generative vision-Language Reasoning

Learning by Correction: Efficient Tuning Task for Zero-Shot ...

引用

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

作者： Li, Rongjie Wu, Yu He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improving their zero-shot reasoning typically requires second-stage instruction tuning, which relies heavily on human-labeled or large language model-generated annotation, incurring high labeling costs. To tackle this challenge, we introduce Image-Conditioned Caption Correction (ICCC), a novel pre-training task designed to enhance VLMs' zero-shot performance without the need for labeled task-aware data. The ICCC task compels VLMs to rectify mismatches between visual and language concepts, thereby enhancing instruction following and text generation conditioned on visual inputs. Leveraging language structure and a lightweight dependency parser, we construct data samples of ICCC task from image-text datasets with low labeling and computation costs. Experimental results on BLIP2 and InstructBLIP demonstrate significant improvements in zero-shot image-text generation-based VL tasks through ICCC instruction tuning.

关键词： Multimodal Reasoning vision-Language

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A Comprehensive Analysis of Factors Impacting Membership Inference

A Comprehensive Analysis of Factors Impacting Membership Inf...

引用

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

作者： DeAlcala, Daniel Mancera, Gonzalo Morales, Aythami Fierrez, Julian Tolosana, Ruben Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain

ISBN: (纸本)9798350365474

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Face recognition Fairness Membership Inference MIA MINT Realiability

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Hairy Ground Truth Enhancement for Semantic Segmentation

Hairy Ground Truth Enhancement for Semantic Segmentation

引用

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

作者： Fischer, Sophie Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England

ISBN: (纸本)9798350365474

Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks;yet many models consider two false positive (or false negative) pixel predictions as 'equally wrong' regardless of the individual pixels' relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous group. We propose the Hairy transform, a novel method to enhance ground truths using 3D 'hairs' to represent each pixel's position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset, as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one-off pre-processing step, and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct 'standard' masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.

关键词： computer vision Ground Truth Enhancement Machine Learning Medical Imaging

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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

作者： Raghavan, Siddeshwar He, Jiangpeng Zhu, Fengqing Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online (1)

关键词： computer vision long-tailed image classification online continual learning

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Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

Generative Rendering: Controllable 4D-Guided Video Generatio...

引用

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

作者： Cai, Shengqu Ceylan, Duygu Gadelha, Matheus Huang, Chun-Hao Paul Wang, Tuanfeng Yang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350353013;9798350353006

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path. Project page: ***/generative_rendering.

关键词： Animation computer Graphics computer vision Generative Models Video Generation Video Synthesis

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Towards Engineered Safe AI with Modular Concept Models

Towards Engineered Safe AI with Modular Concept Models

引用

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

作者： Heidemann, Lena Kurzidem, Iwo Monnet, Maureen Roscher, Karsten Guennemann, Stephan Fraunhofer IKS Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350365474

The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-based computer vision (CV) approaches to become prevalent in safety-critical domains like autonomous driving, despite their high performance. A crucial challenge in these domains is the safety assurance of ML-based systems. To address this, recent safety standardization in the automotive domain has introduced an ML safety lifecycle following an iterative development process. While this approach facilitates safety assurance, its iterative nature requires frequent adaptation and optimization of the ML function, which might include costly retraining of the ML model and is not guaranteed to converge to a safe AI solution. In this paper, we propose a modular ML approach which allows for more efficient and targeted measures to each of the modules and process steps. Each module of the modular concept model represents one visual concept and is aggregated with the other modules' outputs into a task output. The design choices of a modular concept model can be categorized into the selection of the concept modules, the aggregation of their output and the training of the concept modules. Using the example of traffic sign classification, we present each step of the involved design choices and the corresponding targeted measures to take in an iterative development process for engineering safe AI.

关键词： computer vision Concept Models Deep Neural Networks Explainable AI Interpretable Models Machine Learning ML Safety Modular Concept Models Modular Deep Learning Safe AI Safe ML

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

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

作者： Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England

ISBN: (纸本)9798350365474

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Agriculture computer vision ControlNet Deep Learning Diffusion LoRA Plant Phenotyping

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

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

作者： Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada

ISBN: (纸本)9798350365474

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset's distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/ himsR/DataCVChallenge-2024/tree/main

关键词： computer vision Data Search deep learning domain Transfer

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