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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

引用

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

作者： Xu, Yuehuan Jiang, Shuai Cui, Zhe Su, Fei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (纸本)9798350365474

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Action recognition Deep learning Distracted driver behavior

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Inverse Rendering of Translucent Objects using Physical and Neural Renderers

Inverse Rendering of Translucent Objects using Physical and ...

引用

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

作者： Li, Chenhao Trung Thanh Ngo Nagahara, Hajime Osaka Univ Inst Databil Sci Suita Osaka Japan

ISBN: (纸本)9798350301298

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model. Code and Data are available at https://***/ligoudaner377/homo_translucent

关键词： vision + graphics

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

引用

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

作者： Maidment, Tristan Patel, Purav J. Walker, Erin Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Comp Sci Pittsburgh PA 15260 USA Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA 15260 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350365474

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： action clip cnn comunication dataset embeddings gesture hci language-aligned multimodal non-verbal recognition semantic tcn transformers

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Zero-Shot Audio-Visual Compound Expression recognition Method based on Emotion Probability Fusion

Zero-Shot Audio-Visual Compound Expression Recognition Metho...

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

作者： Ryumina, Elena Markitantov, Maxim Ryumin, Dmitry Kaya, Heysem Karpov, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

ISBN: (纸本)9798350365474

A Compound Expression recognition (CER) as a sub-field of affective computing is a novel task in intelligent human-computer interaction and multimodal user interfaces. We propose a novel audio-visual method for CER. Our method relies on emotion recognition models that fuse modalities at the emotion probability level, while decisions regarding the prediction of compound expressions are based on the pair-wise sum of weighted emotion probability distributions. Notably, our method does not use any training data specific to the target task. Thus, the problem is a zero-shot classification task. The method is evaluated in multi-corpus training and cross-corpus validation setups. We achieved F1 scores of 32.15% and 25.56% for the AffWild2 and C-EXPR-DB test subsets without training on target corpus and target task, respectively. Therefore, our method is on par with methods developed training target corpus or target task. The source code is publicly available from https: //***/AVCER/.

关键词： audio-visual emotion recognition compound expression recognition zero-shot classification

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： image copy detection image retrieval

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MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating vision-Language Transformer

MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for...

引用

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

作者： Cao, Jianjian Ye, Peng Li, Shengze Yu, Chong Tang, Yansong Lu, Jiwen Chen, Tao Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

vision-Language Transformers (VLTs) have shown great success recently, but are meanwhile accompanied by heavy computation costs, where a major reason can be attributed to the large number of visual and language tokens. Existing token pruning research for compressing VLTs mainly follows a single-modality-based scheme yet ignores the critical role of aligning different modalities for guiding the token pruning process, causing the important tokens for one modality to be falsely pruned in another modality branch. Meanwhile, existing VLT pruning works also lack the flexibility to dynamically compress each layer based on different input samples. To this end, we propose a novel framework named Multimodal Alignment-Guided Dynamic Token Pruning (MADTP) for accelerating various VLTs. Specifically, we first introduce a well-designed Multi-modality Alignment Guidance (MAG) module that can align features of the same semantic concept from different modalities, to ensure the pruned tokens are less important for all modalities. We further design a novel Dynamic Token Pruning (DTP) module, which can adaptively adjust the token compression ratio in each layer based on different input instances. Extensive experiments on various benchmarks demonstrate that MADTP significantly reduces the computational complexity of kinds of multimodal models while preserving competitive performance. Notably, when applied to the BLIP model in the NLVR2 dataset, MADTP can reduce the GFLOPs by 80% with less than 4% performance degradation. The code is available at https://***/double125/MADTP.

关键词： Model Compress Token Pruning

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OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

OPERA: Alleviating Hallucination in Multi-Modal Large Langua...

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

作者： Hu, Qidong Dong, Xiaoyi Zhang, Pan Wang, Bin He, Conghui Wang, Jiaqi Lin, Dahua Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Anhui Prov Key Lab Digital Secur Hefei Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources, incurring inevitable additional costs. In this paper, we present OPERA, a novel MLLM decoding method grounded in an Overtrust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix, i.e., MLLMs tend to generate new tokens by focusing on a few summary tokens, but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previ-ously generated tokens, and re-allocate the token selection if necessary. With extensive experiments, OPERA shows significant hallucination-mitigating performance on differ-ent MLLMs and metrics, proving its effectiveness and gen-erality. Our code is at: https://github. com/shikiw/OPERA.

关键词： Hallucination Large vision-language model LLM Multimodal LLM

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3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

3D Highlighter: Localizing Regions on 3D Shapes via Text Des...

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

作者： Decatur, Dale Lang, Itai Hanocka, Rana Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350301298

We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https: //***/threedle/3DHighlighter.

关键词： vision + graphics

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

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

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Outsmarting Biometric Imposters: Enhancing Iris-recognition System Security through Physical Adversarial Example Generation and PAD Fine-Tuning

Outsmarting Biometric Imposters: Enhancing Iris-Recognition ...

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

作者： Ogino, Yuka Kakizaki, Kazuya Toizumi, Takahiro Ito, Atsushi NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350365474

In this paper, we address the vulnerabilities of iris recognition systems to both image-based impersonation attacks and Presentation Attacks (PAs) in physical environments. While existing Presentation Attack Detection (PAD) methods have been effective against PAs, they remain susceptible to adversarial examples. We propose a combination of physical adversarial attacks tailored to iris recognition and PAD, and also propose a defense method against them. Our attack methods involve a physical impersonation attack using adversarial perturbation on the iris region and a physical PAD evading attack using an adversarial patch on the pupil region. We demonstrate the high transferability and effectiveness of our attacks on multiple PA instruments in digital and distinct physical environments using multiple recognition engines. To counteract these attacks, we develop a defense method for PAD involving adversarial fine-tuning against both the physical attacks. This defense method successfully reduces the PAD evasion attack success rate from 71.5% to 21.0% in physical environments and ultimately lowers the overall physical impersonation success rate from 58.0% to 19.5%. Our proposed method lays the groundwork for developing more robust and secure iris recognition systems with increased protection against sophisticated PAs.

关键词： Optical character recognition

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