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BEHAVIOR vision Suite: Customizable Dataset Generation via Simulation

BEHAVIOR Vision Suite: Customizable Dataset Generation via S...

引用

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

作者： Ge, Yunhao Tang, Yihe Xu, Jiashu Gokmen, Cem Li, Chengshu Ai, Wensi Martinez, Benjamin Jose Aydin, Arman Anvari, Mona Chakravarthy, Ayush K. Yu, Hong-Xing Wong, Josiah Srivastava, Sanjana Lee, Sharon Zhang, Shengxin Itti, Laurent Li, Yunzhu Martin-Martins, Roberto Liu, Miao Zhang, Pengchuan Zhang, Ruohan Fei-Fei, Li Wu, Jiajun Stanford Univ Stanford CA 94305 USA Univ Southern Calif Los Angeles CA 90007 USA Harvard Univ Cambridge MA 02138 USA Meta GenAI Menlo Pk CA USA Meta FAIR Menlo Pk CA USA Univ Texas Austin Austin TX USA Univ Illinois Urbana IL USA

ISBN: (纸本)9798350353006

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as "filled" and "folded"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://***/

关键词： benchmarking embodied AI simulation synthetic data generation

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

引用

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

作者： Hong, SangHwa Seoul Natl Univ Sci & Technol Dept Ind Engn Gongreung Ro 232 Seoul South Korea

ISBN: (纸本)9798350365474

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting -especially arising severely from data collected in uncontrolled, In-the-wild settings -significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and Affect-Net datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs' generalization and reducing overfitting.

关键词： FER Overfitting Reinforcement Learning

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GLaMM: Pixel Grounding Large Multimodal Model

GLaMM: Pixel Grounding Large Multimodal Model

引用

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

作者： Rasheed, Hanoona Maaz, Muhammad Shaji, Sahal Shaker, Abdelrahman Khan, Salman Cholakkal, Hisham Anwer, Rao M. Xing, Eric Yang, Ming-Hsuan Khan, Fahad S. Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Aalto Univ Espoo Finland Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Merced Merced CA USA Linkoping Univ Linkoping Sweden Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recently, region-level LMMs have been used to generate visually grounded responses. However, they are limited to only referring to a single object cate-gory at a time, require users to specify the regions, or can-not offer dense pixel-wise object grounding. In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks. GLaMM not only grounds objects appearing in the conversations but is flexible enough to accept both textual and optional visual prompts (region of interest) as input. This empowers users to interact with the model at various levels of granularity, both in textual and visual domains. Due to the lack of standard benchmarks for the novel setting of visually Grounded Conversation Generation (GCG), we introduce a comprehensive evaluation protocol with our cu-rated grounded conversations. Our proposed GCG task requires densely grounded concepts in natural scenes at a large-scale. To this end, we propose a densely annotated Grounding-anything Dataset (GranD) using our proposed automated annotation pipeline that encompasses 7.5M unique concepts grounded in a total of 810M regions available with segmentation masks. Besides GCG, GLaMM also performs effectively on several downstream tasks, e. g., referring expression segmentation, image and region-level captioning and vision-language conversations.

关键词： automated dataset annotation LMM MLMM multimodal foundation models Multimodal LMM vision and language vision-language VLM

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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Good at captioning, bad at counting: Benchmarking GPT-4V on ...

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

作者： Zhang, Chenhui Wang, Sherrie MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350365474

Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess world knowledge that leads to strong performance on location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting. Our benchmark is publicly available on this website. A full version of this paper can be found here.

关键词： benchmark Earth observation foundation model vision-language model

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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

Augmented Self-Mask Attention Transformer for Naturalistic D...

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

作者： Zhang, Tiantian Wang, Qingtian Dong, Xiaodong Yu, Wenqing Sun, Hao Zhou, Xuyang Zhen, Aigong Cui, Shun Wu, Dong He, Zhongjiang China Telecom Artificial Intelligence Technol Bei Beijing Peoples R China

ISBN: (纸本)9798350365474

Nowadays, naturalistic driving action recognition and computer vision techniques provide crucial solutions to identify and eliminate distracting driving behavior. Existing methods often extract features through fixed-size sliding windows and predict an action's start and end time. However, the information about a fixed-size window may be incomplete or redundant and the connections between different windows are insufficient. To alleviate this problem, we propose a novel Augmented Self-Mask Attention (AMA) architecture that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. We employ an ensemble technique and use a weighted boundaries fusion to combine and refine predictions with high confidence scores action boundaries. On the test dataset of AI City Challenge 2024 Track3, we achieved significant results compared with other teams, the proposed model ranks first on the public leaderboard of the challenge. Codes are available at https://***/wolfworld6/AIcity2024-track3.

关键词： Action recognition Self-supervised Learning Video Understanding

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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Neural Fourier Filter Bank

Neural Fourier Filter Bank

引用

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

作者： Wu, Zhijie Jin, Yuhe Yi, Kwang Moo Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://***/ubc-vision/NFFB.

关键词： vision + graphics

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

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

作者： Sharif, S. M. A. Myrzabekov, Azamat Khujaev, Nodirkhuja Tsoy, Roman Kim, Seongwan Lee, Jaeho LG Sciencepk Seoul South Korea

ISBN: (纸本)9798350365474

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Edge Device Edge LLIE Edge vision Task Low-light Image Enhancement Optimized LLIE Quantization

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Rethinking the Domain Gap in Near-infrared Face recognition

Rethinking the Domain Gap in Near-infrared Face Recognition

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

作者： Tarasiou, Michail Deng, Jiankang Zafeiriou, Stefanos Imperial Coll London London England

ISBN: (纸本)9798350365474

Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR). While much of the existing literature on HFR identifies the domain gap as a primary challenge and directs efforts towards bridging it at either the input or feature level, our work deviates from this trend. We observe that large neural networks, unlike their smaller counterparts, when pretrained on large scale homogeneous VIS data, demonstrate exceptional zero-shot performance in HFR, suggesting that the domain gap might be less pronounced than previously believed. By approaching the HFR problem as one of low-data fine-tuning, we introduce a straightforward framework: comprehensive pre-training, succeeded by a regularized fine-tuning strategy, that matches or surpasses the current state-of-the-art on four publicly available benchmarks. Given its simplicity and demonstrably strong performance, our method could be used as a practical solution for adjusting face recognition models to HFR as well as a new baseline for future HFR research. Corresponding training and evaluation codes can be found at https://***/michaeltrs/RethinkNIRVIS.

关键词： face recognition heterogeneous face recognition NIR face recognition transfer learning

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MMVP: A Multimodal MoCap Dataset with vision and Pressure Sensors

MMVP: A Multimodal MoCap Dataset with Vision and Pressure Se...

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

作者： Zhang, He Ren, Shenghao Yuan, Haolei Zhao, Jianhui Li, Fan Sun, Shuangpeng Liang, Zhenghao Yu, Tao Shen, Qiu Cao, Xun Beihang Univ Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Weilan Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350353006

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://***/MMVP-Dataset/

关键词： foot contact Motion capture multimodal pressure

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