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Evaluating and Improving Compositional Text-to-Visual Generation

Evaluating and Improving Compositional Text-to-Visual Genera...

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

作者： Baiqi Li Zhiqiu Lin Deepak Pathak Jiayao Li Yixin Fei Kewen Wu Xide Xia Pengchuan Zhang Graham Neubig Deva Ramanan CMU Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-visual models now produce photo-realistic images and videos, they struggle with compositional text prompts involving attributes, relationships, and higher-order reasoning such as logic and comparison. In this work, we conduct an extensive human study on GenAI-Bench to evaluate the performance of leading image and video generation models in various aspects of compositional text-to-visual generation. We also compare automated evaluation metrics against our collected human ratings and find that VQAScore – a metric measuring the likelihood that a VQA model views an image as accurately depicting the prompt – significantly outperforms previous metrics such as CLIP-Score. In addition, VQAScore can improve generation in a black-box manner (without finetuning) via simply ranking a few (3 to 9) candidate images. Ranking by VQAScore is 2x to 3x more effective than other scoring methods like PickScore and ImageReward at improving human ratings for DALL-E 3 and Stable Diffusion, especially on compositional prompts that require advanced visio-linguistic reasoning. Lastly, we identify areas for improvement in VQAScore, such as addressing fine-grained visual details. Despite mild limitations, VQAScore serves as the best automated metric as well as reward function for improving prompt alignment. We will release over 80,000 human ratings to facilitate scientific benchmarking of both generative models and automated metrics.

关键词： Measurement Visualization Toxicology Closed box Footwear Cognition pattern recognition

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

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

作者： Germain Haessig Damien Joubert Justin Haque Moritz B. Milde Tobi Delbruck Viktor Gruev AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Institute of Neuroinformatics University of Zurich and ETH Zurich Switzerland International Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired frame-based cameras lack the high dynamic range and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ~12ms and dynamic range to ~72dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with submillisecond latencies over a millionfold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low-cost and low-latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image high dynamic range polarization scenes that vary at high speeds and to observe the dynamical properties of single collagen fibers in a bovine tendon under rapid cyclical ***: https://***/mFuCeTMWEqY

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The New Agronomists: Language Models are Experts in Crop Management

The New Agronomists: Language Models are Experts in Crop Man...

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

作者： Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan University of Illinois at Urbana-Champaign Cornell University University of South Florida University of Alabama

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing simple neural-network-based reinforcement learning (RL) agents. Building on this foundation, this paper introduces a more advanced intelligent crop management system. This system uniquely combines RL, a language model (LM), and crop simulations facilitated by the Decision Support System for Agrotechnology Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model’s capacity to understand states and explore optimal management practices. The empirical results reveal that the LM exhibits superior learning capabilities. Through simulation experiments with maize crops in Florida (US) and Zaragoza (Spain), the LM not only achieves state-of-the-art performance under various evaluation metrics but also demonstrates a remarkable improvement of over 49% in economic profit, coupled with reduced environmental impact when compared to baseline methods. Our code is available at https://***/jingwu6/LM_AG.

关键词： Decision support systems Profitability Noise Green products Crops Deep reinforcement learning pattern recognition

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NeighborTrack: Single Object Tracking by Bipartite Matching with Neighbor Tracklets and Its Applications to Sports

NeighborTrack: Single Object Tracking by Bipartite Matching ...

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

作者： Yu-Hsi Chen Chien-Yao Wang Cheng-Yun Yang Hung-Shuo Chang Youn-Long Lin Yung-Yu Chuang Hong-Yuan Mark Liao Institute of Information Science Academia Sinica Taiwan Department of Electrical Engineering Purdue University Department of Computer Science National Tsing Hua University Department of Computer Science and Information Engineering National Taiwan University

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors’ information to reconfirm the tracking target and reduces false tracking when the target is occluded. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of 1.92% EAO and 2.11% robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art 72.25% AUC on LaSOT and 75.7% AO on GOT-10K. Most of the tracking examples we have used are related to sports.

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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

WEDGE: A multi-weather autonomous driving dataset built from...

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

作者： Aboli Marathe Deva Ramanan Rahee Walambe Ketan Kotecha Machine Learning Department Carnegie Mellon University PA Robotics Institute Carnegie Mellon University PA Symbiosis Centre for Applied AI (SCAAI) Symbiosis International University (SIU) India Symbiosis Institute of Technology (SIT) Symbiosis International University (SIU) India

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI’s terms 1 of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://***/WEDGE.

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BinaryViT: Pushing Binary vision Transformers Towards Convolutional Models

BinaryViT: Pushing Binary Vision Transformers Towards Convol...

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

作者： Phuoc-Hoan Charles Le Xinlin Li Huawei Noah’s Ark Lab Montreal Research Center Huawei Noah’s Ark Lab

With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources. Binarization can be used to help reduce the size of ViT models and their computational cost significantly, using popcount operations when the weights and the activations are in binary. However, ViTs suffer a larger performance drop when directly applying convolutional neural network (CNN) binarization methods or existing binarization methods to binarize ViTs compared to CNNs on datasets with a large number of classes such as ImageNet-1k. With extensive analysis, we find that binary vanilla ViTs such as DeiT miss out on a lot of key architectural properties that CNNs have that allow binary CNNs to have much higher representational capability than binary vanilla ViT. Therefore, we propose BinaryViT, in which inspired by the CNN architecture, we include operations from the CNN architecture into a pure ViT architecture to enrich the representational capability of a binary ViT without introducing convolutions. These include an average pooling layer instead of a token pooling layer, a block that contains multiple average pooling branches, an affine transformation right before the addition of each main residual connection, and a pyramid structure. Experimental results on the ImageNet-1k dataset show the effectiveness of these operations that allow a fully-binary pure ViT model to be competitive with previous state-of-the-art binary (SOTA) CNN models.

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Video Based Computational Coding of Movement Anomalies in ASD Children

Video Based Computational Coding of Movement Anomalies in AS...

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

作者： Priya Singh Abhishek Pathak Umer Jon Ganai Braj Bhushan Venkatesh K. Subramanian TCS Research India Airbase Labs India Pvt Ltd India Indian Institute of Technology Kanpur India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Early detection and diagnosis are instrumental in early intervention, yet diagnosis often remains delayed due to the limited availability of clinical practitioners and specialists. We propose a computer vision and Machine Learning based novel framework for quantitative screening of autism spectrum disorder (ASD). This is aimed to minimize the need for trained professionals at the initial stage but not substitute for it. We designed simple activities in consultation with ASD clinical psychologists and therapists for children in the 3-7 years age group that could be performed in their natural environment (home). The temporal features extracted from these activities encode the behavioral differences between Autism Spectrum Disorder (ASD) and Typically Developing (TD) control groups. Due to the unavailability of a public dataset of children performing the designed task, we created our own video dataset of 210 videos taken in unconstrained natural settings. The dataset was collected from a single RGB camera. The proposed vision and learning-based algorithms extract features from the collected data for a comprehensive set of indicators including the visual attention span, name-calling response, neck pose of the subjects, gross motor movement and establish a parametrized automated protocol for early detection without the need to take the subjects out of their natural daily environment. This forestalls the possibility of mis-performance by the subject out of nervousness due to unfamiliar surroundings. Results show that our ASD screening methodology can achieve superior performance compared to the single phenotype approaches, and thus has a prognostic value that could be helpful for both clinical and research applications.

关键词： Autism computer vision Visualization Protocols Phenotypes Psychology Variable speed drives

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NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

NTIRE 2023 Challenge on 360° Omnidirectional Image and Vide...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Cao, Mingdeng Mou, Chong Yu, Fanghua Wang, Xintao Zheng, Yinqiang Zhang, Jian Dong, Chao Li, Gen Shan, Ying Timofte, Radu Sun, Xiaopeng Li, Weiqi Sheng, Xuhan Chen, Bin Ma, Haoyu Cheng, Ming Zhao, Shijie Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Wu, Renlong Yang, Yi Zhang, Zhilu Zhang, Shuohao Li, Junyi Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Yang, Hao-Hsiang Chen, Yi-Chung Huang, Zhi-Kai Chen, Wei-Ting Chiang, Yuan-Chun Chang, Hua-En Chen, I.-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Zhang, Zebin Zhang, Jiaqi Wang, Yuhui Cui, Shuhao Huang, Junshi Zhu, Li Tian, Shuman Yu, Wei Luo, Bingchun Cui, Wanwan Xu, Tianyu Li, Chunyang Bao, Long Sun, Heng Zhang, Zhenyu Wang, Qian The University of Tokyo Japan Arc Lab Tencent Pcg China Peking University China Shenzhen Institute of Advanced Technology Cas China Platform Technologies Tencent Online Video China Computer Vision Lab Ifi & Caidas University of Würzburg Germany ByteDance China Peking University Shenzhen Graduate School China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China Harbin Institute of Technology China Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States ShanghaiTech University China Meituan China Xiaomi Inc

ISBN: (纸本)9798350302493

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets. © 2023 ieee.

关键词： Image compression

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On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

On the Real-Time Semantic Segmentation of Aphid Clusters in ...

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

作者： Raiyan Rahman Christopher Indris Tianxiao Zhang Kaidong Li Brian McCornack Daniel Flippo Ajay Sharda Guanghui Wang Department of Computer Science Toronto Metropolitan University Toronto Canada Department of Electrical Engineering and Computer Science University of Kansas Lawrence USA Department of Entomology Kansas State University Manhattan USA Department of Biological and Agricultural Engineering Kansas State University Manhattan USA

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.

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Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling

Repeat and Concatenate: 2D to 3D Image Translation with 3D t...

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

作者： Abril Corona-Figueroa Hubert P. H. Shum Chris G. Willcocks Department of Computer Science Durham University Durham UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.

关键词： Training Solid modeling Three-dimensional displays Uncertainty Encoding pattern recognition Iterative methods

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