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Spatial-temporal Concept based Explanation of 3D ConvNets

Spatial-temporal Concept based Explanation of 3D ConvNets

引用

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

作者： Ji, Ying Wang, Yu Kato, Jien Nagoya Univ Nagoya Aichi Japan Hitotsubashi Univ Kunitachi Tokyo Japan Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350301298

Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand;and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://***/yingji425/STCE.

关键词： Explainable computer vision

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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...

引用

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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Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

引用

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

作者： Tripathi, Aditay Singh, Rishubh Chakraborty, Anirban Shenoy, Pradeep Indian Inst Sci CDS Bangalore Karnataka India Google Res India Bangalore Karnataka India

ISBN: (纸本)9798350301298

Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

关键词： Deep learning architectures and techniques

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MAP: Multimodal Uncertainty-Aware vision-Language Pre-training Model

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-traini...

引用

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

作者： Ji, Yatai Wang, Junjie Gong, Yuan Zhang, Lin Zhu, Yanru Wang, Hongfa Zhang, Jiaxing Sakai, Tetsuya Yang, Yujiu Tsinghua Univ Beijing Peoples R China Waseda Univ Tokyo Japan IDEA Gandhinagar India Tencent TEG Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pretraining frameworks and propose suitable pre-training tasks: Distribution-based vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

关键词： Multi-modal learning

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DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D vision

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-bas...

引用

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

作者： Ling, Lu Sheng, Yichen Tu, Zhi Zhao, Wentian Xin, Cheng Wan, Kun Yu, Lantao Guo, Qianyu Yu, Zixun Lu, Yawen Li, Xuanmao Sun, Xingpeng Ashok, Rohan Mukherjee, Aniruddha Kang, Hao Kong, Xiangrui Hua, Gang Zhang, Tianyi Benes, Bedrich Bera, Aniket Purdue Univ W Lafayette IN 47907 USA Adobe Inc San Jose CA USA Rutgers State Univ New Brunswick NJ USA Google Inc Mountain View CA USA Huazhong Univ Sci & Technol Wuhan Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9798350353006

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

关键词： Contrastive Learning

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GamutMLP: A Lightweight MLP for Color Loss Recovery

GamutMLP: A Lightweight MLP for Color Loss Recovery

引用

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

作者： Le, Hoang M. Price, Brian Cohen, Scott Brown, Michael S. York Univ Toronto ON Canada Adobe Res San Jose CA USA

ISBN: (纸本)9798350301298

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image-the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.

关键词： Low-level vision

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Spherical Transformer for LiDAR-based 3D recognition

Spherical Transformer for LiDAR-based 3D Recognition

引用

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

作者： Lai, Xin Chen, Yukang Lu, Fanbin Liu, Jianhui Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at https://***/dvlab-research/***.

关键词： 3D from multi-view and sensors

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Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

Breaching FedMD: Image Recovery via Paired-Logits Inversion ...

引用

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

作者： Takahashi, Hideaki Liu, Jingjing Liu, Yang Univ Tokyo Tokyo Japan Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Shanghai Artificial Intelligence Lab Beijing Peoples R China

ISBN: (纸本)9798350301298

Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning. In this paper, we found that even though sharing output logits of public datasets is safer than directly sharing gradients, there still exists a substantial risk of data exposure caused by carefully designed malicious attacks. Our study shows that a malicious server can inject a PLI (Paired-Logits Inversion) attack against FedMD and its variants by training an inversion neural network that exploits the confidence gap between the server and client models. Experiments on multiple facial recognition datasets validate that under FedMD-like schemes, by using paired server-client logits of public datasets only, the malicious server is able to reconstruct private images on all tested benchmarks with a high success rate.

关键词： accountability ethics in vision fairness privacy Transparency

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InVERGe: Intelligent Visual Encoder for Bridging Modalities in Report Generation

InVERGe: Intelligent Visual Encoder for Bridging Modalities ...

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

作者： Deria, Ankan Kumar, Komal Chakraborty, Snehashis Mahapatra, Dwarikanath Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350365474

Medical image captioning plays an important role in modern healthcare, improving clinical report generation and aiding radiologists in detecting abnormalities and reducing misdiagnosis. The complex visual and textual data biases make this task more challenging. Recent advancements in transformer-based models have significantly improved the generation of radiology reports from medical images. However, these models require substantial computational resources for training and have been observed to produce unnatural language outputs when trained solely on raw image-text pairs. Our aim is to generate more detailed reports specific to images and to explain the reasoning behind the generated text through image-text alignment. Given the high computational demands of end-to-end model training, we introduce a two-step training methodology with an Intelligent Visual Encoder for Bridging Modalities in Report Generation (InVERGe) model. This model incorporates a lightweight transformer known as the Cross-Modal Query Fusion Layer (CMQFL), which utilizes the output from a frozen encoder to identify the most relevant text-grounded image embedding. This layer bridges the gap between the encoder and decoder, significantly reducing the workload on the decoder and enhancing the alignment between vision and language. Our experimental results, conducted using the MIMIC-CXR, Indiana University chest X-ray images, and CDD-CESM breast images datasets, demonstrate the effectiveness of our approach. Code: https://***/labsroy007/InVERGe

关键词： computer vision Deep Learning Large Language Model Medical Imaging Report Generation

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Zero-shot Model Diagnosis

Zero-shot Model Diagnosis

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

作者： Luo, Jinqi Wang, Zhaoning Wu, Chen Henry Huang, Dong De la Torre, Fernando Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.

关键词： Explainable computer vision

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