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Human-like Controllable Image Captioning with Verb-specific Semantic Roles

Human-like Controllable Image Captioning with Verb-specific ...

引用

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

作者： Chen, Long Jiang, Zhihong Xiao, Jun Liu, Wei Zhejiang Univ Hangzhou Peoples R China Tencent AI Lab Bellevue WA USA Columbia Univ New York NY 10027 USA Tencent Data Platform New York NY USA

ISBN: (纸本)9781665445092

Controllable Image Captioning (CIC) - generating image descriptions following designated control signals- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective properties, such as contents of interest or descriptive patterns. However, we argue that almost all existing objective control signals have overlooked two indispensable characteristics of an ideal control signal: 1) Event-compatible: all visual contents referred to in a single sentence should be compatible with the described activity. 2) Sample-suitable: the control signals should be suitable for a specific image sample. To this end, we propose a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists of a verb and some semantic roles, which represents a targeted activity and the roles of entities involved in this activity. Given a designated VSR, we first train a grounded semantic role labeling (GSRL) model to identify and ground all entities for each role. Then, we propose a semantic structure planner (SSP) to learn human-like descriptive semantic structures. Lastly, we use a roleshift captioning model to generate the captions. Extensive experiments and ablations demonstrate that our framework can achieve better controllability than several strong baselines on two challenging CIC benchmarks. Besides, we can generate multi-level diverse captions easily.

关键词： Visualization computer vision Codes Semantics Benchmark testing Controllability pattern recognition

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Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Look Closer to Segment Better: Boundary Patch Refinement for...

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

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin Tsinghua Univ THU Bosch JCML Ctr State Key Lab Intelligent Technol & Syst Dept Comp Sci & TechnolInst AIBNRist Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China

ISBN: (纸本)9781665445092

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR.

关键词： Measurement computer vision Codes Benchmark testing Business process re-engineering pattern recognition Spatial resolution

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The Multi-Temporal Urban Development SpaceNet Dataset

The Multi-Temporal Urban Development SpaceNet Dataset

引用

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

作者： Van Etten, Adam Hogan, Daniel Manso, Jesus Martinez Shermeyer, Jacob Weir, Nicholas Lewis, Ryan In Q Tel CosmiQ Works Arlington VA 22003 USA Planet San Francisco CA 94107 USA Capella Space San Francisco CA USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes approximate to 24 images (one per month) covering > 100 unique geographies, and comprises > 40,000 km(2) of imagery and exhaustive polygon labels of building footprints therein, totaling over I IM individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution;this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time.

关键词： Multiuser detection computer vision Satellites Change detection algorithms Buildings Time series analysis Sociology

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Audio Provenance Analysis in Heterogeneous Media Sets

Audio Provenance Analysis in Heterogeneous Media Sets

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Milica Gerhardt Luca Cuccovillo Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a framework for Audio Provenance Analysis, addressing the complex challenge of ana-lyzing heterogeneous sets of audio items without requiring any prior knowledge of their content. Our framework applies a novel approach that combines partial audio matching and phylogeny techniques. It constructs directed acyclic graphs to capture the origins and the evolution of content within near-duplicate audio clusters, identifying the least altered versions and tracing the reuse of content within these clusters. The approach is evaluated for two selected application scenarios, demonstrating that it can accurately determine the direction of content reuse and identify parent-child relationships, while also offering a dedicated dataset for benchmarking future research in this area.

关键词： Directed acyclic graph computer vision conferences Media Benchmark testing Phylogeny pattern recognition

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Person30K: A Dual-Meta Generalization Network for Person Re-Identification

Person30K: A Dual-Meta Generalization Network for Person Re-...

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

作者： Bai, Yan Jiao, Jile Ce, Wang Liu, Jun Lou, Yihang Feng, Xuetao Duan, Ling-Yu Peking Univ Beijing Peoples R China Alibaba Grp Beijing Peoples R China Singapore Univ Technol & Design Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple aspects, including limited data scale, capture condition variations, and appearance diversities. To this end, we collect a new dataset named Person30K with the following distinct features: 1) a very large scale containing 1.38 million images of 30K identities, 2) a large capture system containing 6,497 cameras deployed at 89 different sites, 3) abundant sample diversities including varied backgrounds and diverse person poses. Furthermore, we propose a domain generalization ReID method, dual-meta generalization network (DMG-Net), to exploit the merits of meta-learning in both the training procedure and the metric space learning. Concretely, we design a "learning then generalization evaluation" meta-training procedure and a meta-discrimination loss to enhance model generalization and discrimination capabilities. Comprehensive experiments validate the effectiveness of our DMG-Net.

关键词： Training computer vision Computational modeling Benchmark testing Extraterrestrial measurements Cameras Data models

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Backdoor Attacks Against Deep Learning Systems in the Physical World

Backdoor Attacks Against Deep Learning Systems in the Physic...

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

作者： Wenger, Emily Passananti, Josephine Bhagoji, Arjun Nitin Yao, Yuanshun Zheng, Haitao Zhao, Ben Y. Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9781665445092

Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific "trigger." Existing works on backdoor attacks and defenses, however, mostly focus on digital attacks that apply digitally generated patterns as triggers. A critical question remains unanswered: "can backdoor attacks succeed using physical objects as triggers, thus making them a credible threat against deep learning systems in the real world?" We conduct a detailed empirical study to explore this question for facial recognition, a critical deep learning task. Using 7 physical objects as triggers, we collect a custom dataset of 3205 images of 10 volunteers and use it to study the feasibility of "physical" backdoor attacks under a variety of real-world conditions. Our study reveals two key findings. First, physical backdoor attacks can be highly successful if they are carefully configured to overcome the constraints imposed by physical objects. In particular, the placement of successful triggers is largely constrained by the target model's dependence on key facial features. Second, four of today's state-of-the-art defenses against (digital) backdoors are ineffective against physical backdoors, because the use of physical objects breaks core assumptions used to construct these defenses. Our study confirms that (physical) backdoor attacks are not a hypothetical phenomenon but rather pose a serious real-world threat to critical classification tasks. We need new and more robust defenses against backdoors in the physical world.

关键词： Deep learning computer vision Face recognition Robustness Task analysis Facial features

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Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

Monocular 3D Multi-Person Pose Estimation by Integrating Top...

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

作者： Cheng, Yu Wang, Bo Yang, Bo Tan, Robby T. Natl Univ Singapore Singapore Singapore Tencent Game AI Res Ctr Shenzhen Peoples R China Yale NUS Coll Singapore Singapore

ISBN: (纸本)9781665445092

In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detection, but they process all persons at once at the same scale, causing them to be sensitive to multiple-persons scale variations. To address these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for a single person, and consequently cannot assess natural interperson interactions, we propose a two-person pose discriminator that enforces natural two-person interactions. Lastly, we also apply a semi-supervised method to overcome the 3D ground-truth data scarcity. Quantitative and qualitative evaluations show the effectiveness of the proposed method. Our code is available publicly.

关键词： Heating systems computer vision Three-dimensional displays Codes Pose estimation pattern recognition

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Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

Multi-Modal Fusion Transformer for End-to-End Autonomous Dri...

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

作者： Prakash, Aditya Chitta, Kashyap Geiger, Andreas Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany

ISBN: (纸本)9781665445092

How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this work, we demonstrate that imitation learning policies based on existing sensor fusion methods under-perform in the presence of a high density of dynamic agents and complex scenarios, which require global contextual reasoning, such as handling traffic oncoming from multiple directions at uncontrolled intersections. Therefore, we propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.

关键词： Three-dimensional displays Laser radar Object detection Sensor fusion Transformers Sensors pattern recognition

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Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

Removing the Background by Adding the Background: Towards Ba...

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

作者： Wang, Jinpeng Gao, Yuting Li, Ke Lin, Yiqi Ma, Andy J. Cheng, Hao Peng, Pai Huang, Feiyue Ji, Rongrong Sun, Xing Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Tencent Youtu Lab Shenzhen Guangdong Peoples R China Xiamen Univ Xiamen Fujian Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To mitigate the model reliance towards the background, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. We term our method as Background Erasing (BE). It is worth noting that the implementation of our method is so simple and neat and can be added to most of the SOTA methods without much efforts. Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased dataset Diving48.

关键词： Deep learning computer vision Force Focusing Resists Predictive models Benchmark testing

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Unsupervised Pre-training for Person Re-identification

Unsupervised Pre-training for Person Re-identification

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

作者： Fu, Dengpan Chen, Dongdong Bao, Jianmin Yang, Hao Yuan, Lu Zhang, Lei Li, Houqiang Chen, Dong Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781665445092

In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re-ID datasets are all of limited scale due to the costly effort required for data annotation. Previous research tries to leverage models pre-trained on ImageNet to mitigate the shortage of person Re-ID data but suffers from the large domain gap between ImageNet and person Re-ID data. LUPerson is an unlabeled dataset of 4M images of over 200K identities, which is 30x larger than the largest existing Re-ID dataset. It also covers a much diverse range of capturing environments (e.g., camera settings, scenes, etc.). Based on this dataset, we systematically study the key factors for learning Re-ID features from two perspectives: data augmentation and contrastive loss. Unsupervised pre-training performed on this large-scale dataset effectively leads to a generic Re-ID feature that can benefit all existing person Re-ID methods. Using our pre-trained model in some basic frameworks, our methods achieve state-of-the-art results without bells and whistles on four widely used Re-ID datasets: CUHK03, Market1501, DukeMTMC, and MSMT17. Our results also show that the performance improvement is more significant on small-scale target datasets or under few-shot setting.

关键词： computer vision Annotations Computational modeling Cameras Data models pattern recognition Videos

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