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Multi-view Classification Using Hybrid Fusion and Mutual Distillation

Multi-view Classification Using Hybrid Fusion and Mutual Dis...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Black, Samuel Souvenir, Richard Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9798350318920;9798350318937

Multi-view classification problems are common in medical image analysis, forensics, and other domains where problem queries involve multi-image input. Existing multi-view classification methods are often tailored to a specific task. In this paper, we repurpose off-the-shelf Hybrid CNN-Transformer networks for multi-view classification with either structured or unstructured views. Our approach incorporates a novel fusion scheme, mutual distillation, and minimal additional parameters. We demonstrate the effectiveness and generalization capability of our approach, MV-HFMD, on multiple multi-view classification tasks and show that it outperforms other multi-view approaches, even task-specific methods. Code is available at https://***/vidarlab/multi-view-hybrid.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Hard-label based Small Query Black-box Adversarial Attack

Hard-label based Small Query Black-box Adversarial Attack

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Park, Jeonghwan Miller, Paul McLaughlin, Niall Queens Univ Belfast Belfast Antrim North Ireland

ISBN: (纸本)9798350318920;9798350318937

We consider the hard-label based black-box adversarial attack setting which solely observes the target model's predicted class. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful attack. One approach to tackle this drawback is utilising the adversarial transferability between white-box surrogate models and black-box target model. However, the majority of the methods adopting this approach are soft-label based to take the full advantage of zeroth-order optimisation. Unlike mainstream methods, we propose a new practical setting of hard-label based attack with an optimisation process guided by a pre-trained surrogate model. Experiments show the proposed method significantly improves the query efficiency of the hard-label based black-box attack across various target model architectures. We find the proposed method achieves approximately 5 times higher attack success rate compared to the benchmarks, especially at the small query budgets as 100 and 250.

关键词： adversarial attack and defense methods Adversarial learning Algorithms Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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RangeViT: Towards vision Transformers for 3D Semantic Segmentation in Autonomous Driving

RangeViT: Towards Vision Transformers for 3D Semantic Segmen...

引用

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

作者： Ando, Angelika Gidaris, Spyros Bursuc, Andrei Puy, Gilles Boulch, Alexandre Marlet, Renaud Valeo Ai Paris France Univ PSL Mines Paris Ctr Robot Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9798350301298

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image-based benchmarks. In this work, we question if projection-based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://***/valeoai/rangevit.

关键词： Deep learning architectures and techniques

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Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering

Prompting Large Language Models with Answer Heuristics for K...

引用

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

作者： Shao, Zhenwei Yu, Zhou Wang, Meng Yu, Jun Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3 [3]) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet-a conceptually simple framework designed to prompt GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively.

关键词： and reasoning language vision

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Enhancing Soft Biometric Face Template Privacy with Mutual Information-Based Image Attacks

Enhancing Soft Biometric Face Template Privacy with Mutual I...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rezgui, Zohra Strisciuglio, Nicola Veldhuis, Raymond Univ Twente Enschede Netherlands Norwegian Univ Sci & Technol Gjovik Norway

ISBN: (纸本)9798350370287;9798350370713

The features learned by deep-learning based face recognition networks pose privacy risks as they encode sensitive information that could be used to infer demographic attributes. In this paper, we propose an image-based solution that enhances the soft biometric privacy of the templates generated by face recognition networks. The method uses a reliable mutual information estimation and simulates a minimization step of the mutual information between the features and the target variable. We comprehensively assess the effectiveness of our approach on the gender classification task by formulating two distinct evaluation settings: one for evaluating the performance of the approach's ability to fool a given gender classifier and another for evaluating its ability to hinder the separability of the gender distributions. We conduct an extensive analysis, considering varying levels of perturbation. We show the potential of our method as a privacy-enhancing method that preserves the verification performance as well as a strong single-step adversarial attack.

关键词： Face recognition

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Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis

Hierarchical Text Spotter for Joint Text Spotting and Layout...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Long, Shangbang Qin, Siyang Fujii, Yasuhisa Bissacco, Alessandro Raptis, Michalis Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350318920;9798350318937

We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and identify its 4-level hierarchical structure: characters, words, lines, and paragraphs. The proposed HTS is characterized by two novel components: (1) a Unified-DetectorPolygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines;(2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words. HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.

关键词： Algorithms Image recognition and understanding

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You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified vision Language Model

You Need Multiple Exiting: Dynamic Early Exiting for Acceler...

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

作者： Tang, Shengkun Wang, Yaqing Kong, Zhenglun Zhang, Tianchi Li, Yao Ding, Caiwen Wang, Yanzhi Liang, Yi Xu, Dongkuan North Carolina State Univ Raleigh NC 27695 USA Google Res New York NY USA Univ Connecticut Mansfield PA USA Univ Michigan Ann Arbor MI USA Univ N Carolina Chapel Hill NC USA Northeastern Univ Boston MA USA

ISBN: (纸本)9798350301298

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full computation of the large-scale model, not all of inputs need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot be applied to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder layers. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To address this issue, we propose a novel early exiting strategy for unified vision language models, which allows to dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely MuE. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce expected inference time by up to 50% and 40% while maintaining 99% and 96% performance respectively.

关键词： Efficient and scalable vision

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BioNet: A Biologically-inspired Network for Face recognition

BioNet: A Biologically-inspired Network for Face Recognition

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

作者： Li, Pengyu Terminus Grp Terminus Labs Beijing Peoples R China

ISBN: (纸本)9798350301298

Recently, whether and how cutting-edge Neuroscience findings can inspire Artificial Intelligence (AI) confuse both communities and draw much discussion. As one of the most critical fields in AI, computer vision (CV) also pays much attention to the discussion. To show our ideas and experimental evidence to the discussion, we focus on one of the most broadly researched topics both in Neuroscience and CV fields, i.e., Face recognition (FR). Neuroscience studies show that face attributes are essential to the human face-recognizing system. How the attributes contribute also be explained by the Neuroscience community. Even though a few CV works improved the FR performance with attribute enhancement, none of them are inspired by the human face-recognizing mechanism nor boosted performance significantly. To show our idea experimentally, we model the biological characteristics of the human face-recognizing system with classical Convolutional Neural Network Operators (CNN Ops) purposely. We name the proposed Biologically-inspired Network as BioNet. Our BioNet consists of two cascade sub-networks, i.e., the Visual Cortex Network (VCN) and the Inferotemporal Cortex Network (ICN). The VCN is modeled with a classical CNN backbone. The proposed ICN comprises three biologically-inspired modules, i.e., the Cortex Functional Compartmentalization, the Compartment Response Transform, and the Response Intensity Modulation. The experiments prove that: 1) The cutting-edge findings about the human face-recognizing system can further boost the CNN-based FR network. 2) With the biological mechanism, both identity-related attributes (e.g., gender) and identity-unrelated attributes (e.g., expression) can benefit the deep FR models. Surprisingly, the identity-unrelated ones contribute even more than the identity-related ones. 3) The proposed BioNet significantly boosts state-of-the-art on standard FR benchmark datasets. For example, BioNet boosts IJB-B@1e-6 from 52.12% to 68.28% and MegaFace fr

关键词： body gesture Humans: Face movement pose

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Towards Visual Saliency Explanations of Face Verification

Towards Visual Saliency Explanations of Face Verification

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Lu, Yuhang Xu, Zewei Ebrahimi, Touradj Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350318920;9798350318937

In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition systems. Recent studies have investigated the usage of visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper concentrates on explainable face verification tasks and conceives a new explanation framework. Firstly, a definition of the saliency-based explanation method is provided, which focuses on the decisions made by the deep FR model. Secondly, a new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Then, an evaluation methodology is designed to measure the performance of general visual saliency explanation methods in face verification. Finally, substantial visual and quantitative results have shown that the proposed CorrRISE method demonstrates promising results in comparison with other state-of-the-art explainable face verification approaches.

关键词： accountable Algorithms Algorithms Biometrics body pose ethical computer vision Explainable face fair gesture privacy-preserving

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Optimizing Long-Term Robot Tracking with Multi-Platform Sensor Fusion

Optimizing Long-Term Robot Tracking with Multi-Platform Sens...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Albanese, Giuliano Mitra, Arka Zaech, Jan-Nico Zhao, Yupeng Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350318920;9798350318937

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

关键词： Algorithms Algorithms and algorithms Applications formulations Machine learning architectures Robotics Video recognition and understanding

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