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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

i-MAE: Are Latent Representations in Masked Autoencoders Lin...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kevin Zhang Zhiqiang Shen Peking University KNQ.AI Mohamed bin Zayed University of AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability.

关键词： Training Measurement computer vision Image recognition conferences Semantics Aerospace electronics

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haoxiang Wang Pavan Kumar Anasosalu Vasu Fartash Faghri Raviteja Vemulapalli Mehrdad Farajtabar Sachin Mehta Mohammad Rastegari Oncel Tuzel Hadi Pouransari University of Illinois Urbana-Champaign Apple

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multitask learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： Training computer vision Visualization Computational modeling Semantic segmentation Semantics Merging

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Evaluating Multimodal Large Language Models across Distribution Shifts and Augmentations

Evaluating Multimodal Large Language Models across Distribut...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Aayush Atul Verma Amir Saeidi Shamanthak Hegde Ajay Therala Fenil Denish Bardoliya Nagaraju Machavarapu Shri Ajay Kumar Ravindhiran Srija Malyala Agneet Chatterjee Yezhou Yang Chitta Baral Arizona State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundational models such as Multimodal Large Language Models (MLLMs) with their ability to interpret images and generate intricate responses has led to their widespread adoption across multiple computer vision and natural language processing tasks. However, they suffer from hallucinations and struggle to reason over complex reasoning tasks. In this work, we evaluate the performance of MLLMs across multiple multimodal augmentations and evaluate their performance in out-of-distribution settings. We benchmark 3 models, across 2 vision-language datasets, VQAv2 and CLEVR, and assess their performance across adversarial transformations in both the vision and language modalities. We introduce image perturbations using various augmentations, including noise addition, blurring, and median filtering and generate adversarial questions which contain conjunctions, disjunctions and negations. Additionally, we conduct a detailed fine-grained analysis to assess the model’s performance on particular question categories, such as those related to shape and color, across images featuring identical or varying objects. Our findings indicate a notable decrease in the performance of current MLLMs for synthetic images, with a gradual decline observed across both vision and language augmentations. Specifically, Gaussian Noise Addition emerges as the most detrimental augmentation, and we observe a significant drop in performance with complex questions containing multiple connectives. In these times of rapid development and deployment of MLLMs in real-world settings, we believe our findings are a first step towards benchmarking the robustness and out-of-distribution behavior of such models.

关键词： computer vision Analytical models Shape Large language models Computational modeling Perturbation methods Benchmark testing

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Scaling Graph Convolutions for Mobile vision

Scaling Graph Convolutions for Mobile Vision

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： William Avery Mustafa Munir Radu Marculescu The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

To compete with existing mobile architectures, Mobile-ViG 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 AP mask . For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU 1 .

关键词： Instance segmentation Accuracy Convolution Semantic segmentation Message passing computer architecture Object detection

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MobileViG: Graph-Based Sparse Attention for Mobile vision Applications

MobileViG: Graph-Based Sparse Attention for Mobile Vision Ap...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mustafa Munir William Avery Radu Marculescu The University of Texas at Austin

Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfortunately, for mobile applications, ViGs are computationally expensive due to the overhead of representing images as graph structures. In this work, we propose a new graph-based sparse attention mechanism, Sparse vision Graph Attention (SVGA), that is designed for ViGs running on mobile devices. Additionally, we propose the first hybrid CNN-GNN architecture for vision tasks on mobile devices, MobileViG, which uses SVGA. Extensive experiments show that MobileViG beats existing ViG models and existing mobile CNN and ViT architectures in terms of accuracy and/or speed on image classification, object detection, and instance segmentation tasks. Our fastest model, MobileViG-Ti, achieves 75.7% top-1 accuracy on ImageNet-1K with 0.78 ms inference latency on iPhone 13 Mini NPU (compiled with CoreML), which is faster than MobileNetV2x1.4 (1.02 ms, 74.7% top-1) and MobileNetV2x1.0 (0.81 ms, 71.8% top-1). Our largest model, MobileViG-B obtains 82.6% top-1 accuracy with only 2.30 ms latency, which is faster and more accurate than the similarly sized EfficientFormer-L3 model (2.77 ms, 82.4%). Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices. Our code is publicly available at https://***/SLDGroup/MobileViG.

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LaDiffGAN: Training GANs with Diffusion Supervision in Latent Spaces

LaDiffGAN: Training GANs with Diffusion Supervision in Laten...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xuhui Liu Bohan Zeng Sicheng Gao Shanglin Li Yutang Feng Hong Li Boyu Liu Jianzhuang Liu Baochang Zhang Beihang University Shenzhen Institute of Advanced Technology Shenzhen China Zhongguancun Laboratory Beijing China Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have recently become increasingly popular in a number of computer vision tasks, but they fail to achieve satisfactory results for unsupervised image-to-image translation, since they require massive training data and rely heavily on extra guidance. In this scenario, GANs can alleviate these issues existing in diffusion models, albeit with suboptimal quality. In this paper, we leverage the advantages of both GANs and diffusion models by training GANs with diffusion supervision in latent spaces (LaDiffGAN) to solve the unsupervised image-to-image translation task. Firstly, to promote style transfer quality, we encode the data in specific latent spaces with styles of the target and source domains. Secondly, we introduce the diffusion process with different amounts of Gaussian noise to enhance the modeling capability of GANs on the complex data distribution. We accordingly design a latent diffusion GAN loss to align the latent features between generated and training images. Lastly, we introduce a heterogeneous conditional denoising loss that incorporates image-level supervision to further improve the quality of generated results. Our LaDiffGAN significantly alleviates the drawbacks associated with diffusion models, such as data leakage, high inference cost, and high dependence on large training data sets. Extensive experiments show that LaDiffGAN outperforms previous GAN models and delivers comparable or even better performance than diffusion models.

关键词： Training computer vision Image synthesis Gaussian noise Noise reduction Training data Diffusion processes

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Learning to Classify New Foods Incrementally Via Compressed Exemplars

Learning to Classify New Foods Incrementally Via Compressed ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Justin Yang Zhihao Duan Jiangpeng He Fengqing Zhu Elmore School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed number of food classes. This contrasts drastically with the reality of food consumption, which features constantly changing data. Therefore, food image classification systems should adapt to and manage data that continuously evolves. This is where continual learning plays an important role. A challenge in continual learning is catastrophic forgetting, where ML models tend to discard old knowledge upon learning new information. While memory-replay algorithms have shown promise in mitigating this problem by storing old data as exemplars, they are hampered by the limited capacity of memory buffers, leading to an imbalance between new and previously learned data. To address this, our work explores the use of neural image compression to extend buffer size and enhance data diversity. We introduced the concept of continuously learning a neural compression model to adaptively improve the quality of compressed data and optimize the bitrates per pixel (bpp) to store more exemplars. Our extensive experiments, including evaluations on food-specific datasets including Food-101 and VFN-74, as well as the general dataset ImageNet-100, demonstrate improvements in classification accuracy. This progress is pivotal in advancing more realistic food recognition systems that are capable of adapting to continually evolving data. Moreover, the principles and methodologies we’ve developed hold promise for broader applications, extending their benefits to other domains of continual machine learning systems.

关键词： Continuing education Adaptation models computer vision Image coding conferences Machine learning Data models

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MvAV-pix2pixHD: Multi-view Aerial View Image Translation

MvAV-pix2pixHD: Multi-view Aerial View Image Translation

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jun Yu Keda Lu Shenshen Du Lin Xu Peng Chang Houde Liu Bin Lan Tianyu Liu University of Science and Technology of China Ping An Technology Co. Ltd China PAII Inc. Tsinghua Shenzhen International Graduate School Jianghuai Advance Technology Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modal aerial view image translation involves converting aerial images from one modality to another while preserving basic details and features. These modalities encompass Synthetic Aperture Radar (SAR), Infrared (IR), Visible Light (RGB), Electro-Optical (EO), and other image types. Recently, various methods have been proposed to tackle this task, but the focus tends to be on paired image research, overlooking the discrepancies found in aerial images of the same location captured at different times and angles, termed incomplete matching or multi-view image translation. Consequently, we propose MvAV-pix2pixHD to address this issue. For multi-view data sampling, we propose two methods: random sampling and time-priority sampling. Additionally, within the pix2pixHD framework, we introduce an inverse generator to ensure the basic semantic features of the generated images and incorporate three robust loss functions to constrain the authenticity of the generated images. We conduct extensive experiments on two multi-view image translation tasks in the Multi-modal Aerial View Imagery Challenge: Translation (MAVIC-T). Experimental results demonstrate the superiority of our proposed method, and we achieved second place in the MAVIC-T competition in the 20th ieee Workshop on Perception Beyond the Visible Spectrum of the CVPR 2024.

关键词： computer vision Image resolution conferences Computational modeling Semantics computer architecture Generators

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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis

Systematic Architectural Design of Scale Transformed Attenti...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrew Hryniowski Alexander Wong Vision and Image Processing Research Group University of Waterloo Waterloo Artificial Intelligence Institute Waterloo DarwinAI Corp. Waterloo

Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale Class Representational Response Similarity Analysis (ClassRep-Sim) which can be used to identify specific design interventions that lead to more efficient self-attention convolutional neural network architectures. Using insights grained from ClassRepSim we propose the Spatial Transformed Attention Condenser (STAC) module, a novel attention-condenser based self-attention module. We show that adding STAC modules to ResNet style architectures can result in up to a 1.6% increase in top-1 accuracy compared to vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to SENet models on the ImageNet64x64 dataset, at the cost of up to 1.7% increase in FLOPs and 2x the number of parameters. In addition, we demonstrate that results from ClassRepSim analysis can be used to select an effective parameterization of the STAC module resulting in competitive performance compared to an extensive parameter search.

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ATOM: Attention Mixer for Efficient Dataset Distillation

ATOM: Attention Mixer for Efficient Dataset Distillation

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Samir Khaki Ahmad Sajedi Kai Wang Lucy Z. Liu Yuri A. Lawryshyn Konstantinos N. Plataniotis University of Toronto National University of Singapore Royal Bank of Canada (RBC)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real dataset. These approaches ultimately aim to attain test accuracy levels akin to those achieved by models trained on the entirety of the original dataset. Previous studies in feature and distribution matching have achieved significant results without incurring the costs of bi-level optimization in the distillation process. Despite their convincing efficiency, many of these methods suffer from marginal downstream performance improvements, limited distillation of contextual information, and subpar cross-architecture generalization. To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process. Spatial-wise attention helps guide the learning process based on consistent localization of classes in their respective images, allowing for distillation from a broader receptive field. Meanwhile, channel-wise attention captures the contextual information associated with the class itself, thus making the synthetic image more informative for training. By integrating both types of attention, our ATOM module demonstrates superior performance across various computer vision datasets, including CIFAR10/100 and Tiny-Imagenet. Notably, our method significantly improves performance in scenarios with a low number of images per class, thereby enhancing its potential. Furthermore, we maintain the improvement on cross-architectures and applications such as neural architecture search.

关键词： Training Location awareness computer vision Costs Atomic layer deposition Performance gain Transformers

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