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Hartwig Adam

Hartwig Adam

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    Preview abstract This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semantics are introduced between pairs of objects. To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs). VLMs provide well-aligned image and text embeddings, where similar relationships are optimized to be close to each other for semantic unification. Our bottom-up design enables the model to enjoy the benefit of training with both object detection and visual relationship datasets. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model. UniVRD achieves 38.07 mAP on HICO-DET, outperforming the current best bottom-up HOI detector by 14.26 mAP. More importantly, we show that our unified detector performs as well as dataset-specific models in mAP, and achieves further improvements when we scale up the model. Our code will be made publicly available on GitHub. View details
    VideoPoet: A Large Language Model for Zero-Shot Video Generation
    Lijun Yu
    Xiuye Gu
    Rachel Hornung
    Hassan Akbari
    Ming-Chang Chiu
    Josh Dillon
    Agrim Gupta
    Meera Hahn
    Anja Hauth
    David Hendon
    Alonso Martinez
    Grant Schindler
    Huisheng Wang
    Jimmy Yan
    Xuan Yang
    Lu Jiang
    arxiv Preprint (2023) (to appear)
    Preview abstract We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/ View details
    Preview abstract We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combines Alternating Gradient Descent (AGD) and Mixture-of-Experts (MoE) for efficient model and task scaling. We conduct extensive empirical studies and reveal the following key insights: 1) Performing gradient descent updates by alternating on diverse modalities, loss functions, and tasks, with varying input resolutions, efficiently improves the model. 2) Sparsification with MoE on a single modality-agnostic encoder substantially improves the performance, outperforming dense models that use modality-specific encoders or additional fusion layers and greatly mitigates the conflicts between modalities. IMP achieves competitive performance on a wide range of downstream tasks including video classification, image classification, image-text, and video-text retrieval. Most notably, we train a sparse IMP-MoE-L variant focusing on video tasks that achieves new state-of-the-art in zero-shot video classification: 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 68.3% on Kinetics-700, improving the previous state-of-the-art by +5%, +6.7%, and +5.8%, respectively, while using only 15% of their total training computational cost. View details
    Surrogate Gap Minimization Improves Sharpness Aware Training
    Juntang Zhuang
    Liangzhe Yuan
    Yin Cui
    Nicha C. Dvornek
    Sekhar Tatikonda
    James S. Duncan
    International Conference on Learning Representations (ICLR) (2022)
    Preview abstract The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a perturbed loss defined as the maximum loss within a neighborhood in the parameter space. However, we show that both sharp and flat minima can have a low perturbed loss, implying that SAM does not always prefer flat minima. Instead, we define a surrogate gap, a measure equivalent to the dominant eigenvalue of Hessian at a local minimum when the radius of neighborhood (to derive the perturbed loss) is small. The surrogate gap is easy to compute and feasible for direct minimization during training. Based on the above observations, we propose Surrogate Gap Guided Sharpness-Aware Minimization (GSAM), a novel improvement over SAM with negligible computation overhead. Conceptually, GSAM consists of two steps: 1) a gradient descent like SAM to minimize the perturbed loss, and 2) an ascent step in the orthogonal direction (after gradient decomposition) to minimize the surrogate gap and yet not affect the perturbed loss. GSAM seeks a region with both small loss (by step 1) and low sharpness (by step 2), giving rise to a model with high generalization capabilities. Theoretically, we show the convergence of GSAM and provably better generalization than SAM.Empirically, GSAM consistently improves generalization (e.g., +3.2% over SAM and +5.4% over AdamW on ImageNet top-1 accuracy for ViT-B/32). Code is released at https://sites.google.com/view/gsam-iclr22/home. View details
    View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
    Jennifer Jianing Sun
    Jiaping Zhao
    Liangzhe Yuan
    Yuxiao Wang
    Liang-Chieh Chen
    International Journal of Computer Vision, vol. 130 (2022), pp. 111-135
    Preview abstract Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant appearance variations across viewpoints that make the recognition tasks challenging. To address this, we explore recognizing similarity in 3D human body poses from 2D information, which has not been well-studied in existing works. Here, we propose an approach to learning a compact view-invariant embedding space from 2D body joint keypoints, without explicitly predicting 3D poses. Input ambiguities of 2D poses from projection and occlusion are difficult to represent through a deterministic mapping, and therefore we adopt a probabilistic formulation for our embedding space. Experimental results show that our embedding model achieves higher accuracy when retrieving similar poses across different camera views, in comparison with 3D pose estimation models. We also show that by training a simple temporal embedding model, we achieve superior performance on pose sequence retrieval and largely reduce the embedding dimension from stacking frame-based embeddings for efficient large-scale retrieval. Furthermore, in order to enable our embeddings to work with partially visible input, we further investigate different keypoint occlusion augmentation strategies during training. We demonstrate that these occlusion augmentations significantly improve retrieval performance on partial 2D input poses. Results on action recognition and video alignment demonstrate that using our embeddings without any additional training achieves competitive performance relative to other models specifically trained for each task. View details
    Contextualized Spatial-Temporal Contrastive Learning with Self-Supervision
    Liangzhe Yuan
    Rui Qian
    Yin Cui
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 13977-13986
    Preview abstract Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes sub-optimal for learning spatio-temporally fine-grained features in videos, where scenes and instances evolve through space and time. In this paper, we present Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively learn spatio-temporally fine-grained video representations via self-supervision. We first design a region-based pretext task which requires the model to transform in-stance representations from one view to another, guided by context features. Further, we introduce a simple network design that successfully reconciles the simultaneous learning process of both holistic and local representations. We evaluate our learned representations on a variety of downstream tasks and show that ConST-CL achieves competitive results on 6 datasets, including Kinetics, UCF, HMDB, AVA-Kinetics, AVA and OTB. View details
    Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
    Yuxiao Wang
    Jiaping Zhao
    Liangzhe Yuan
    Jennifer Jianing Sun
    Xi Peng
    Dimitris N. Metaxas
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
    Preview abstract We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. We further propose two regularization terms to ensure disentanglement and smoothness of the learned representations. The resulting pose representations can be used for cross-view action recognition. To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition. This task trains models with actions from only one single viewpoint while models are evaluated on poses captured from all possible viewpoints. We evaluate the learned representations on standard benchmarks for action recognition, and show that (i) CV-MIM performs competitively compared with the state-of-the-art models in the fully-supervised scenarios;(ii) CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting;(iii) and the learned representations can significantly boost the performance when reducing the amount of supervised training data. Our code is made publicly available at https://github. com/google-research/google-research/tree/master/poem. View details
    Preview abstract Videos can evoke a range of affective responses in viewers. The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation. We introduce the Evoked Expressions from Videos (EEV) dataset, a large-scale dataset for studying viewer responses to videos. Each video is annotated at 6 Hz with 15 continuous evoked expression labels, corresponding to the facial expression of viewers who reacted to the video. We use an expression recognition model within our data collection framework to achieve scalability. In total, there are 36.7 million annotations of viewer facial reactions to 23,574 videos (1,700 hours). We use a publicly available video corpus to obtain a diverse set of video content. We establish baseline performance on the EEV dataset using an existing multimodal recurrent model. Transfer learning experiments show an improvement in performance on the LIRIS-ACCEDE video dataset when pre-trained on EEV. We hope that the size and diversity of the EEV dataset will encourage further explorations in video understanding and affective computing. A subset of EEV is released at https://github.com/google-research-datasets/eev. View details
    Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
    Bowen Cheng
    Maxwell D. Collins
    Yukun Zhu
    Thomas S. Huang
    Liang-Chieh Chen
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
    Preview abstract In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025×2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several top-down approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation. View details
    View-Invariant Probabilistic Embedding for Human Pose
    Jennifer Jianing Sun
    Jiaping Zhao
    Liang-Chieh Chen
    European Conference on Computer Vision (ECCV) (2020)
    Preview abstract Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in human body poses across multiple views. This ability is useful for analyzing body movements and human behaviors in images and videos. In this paper, we propose an approach for learning a compact view-invariant embedding space from 2D joint keypoints alone, without explicitly predicting 3D poses. Since 2D poses are projected from 3D space, they have an inherent ambiguity, which is difficult to represent through a deterministic mapping. Hence, we use probabilistic embeddings to model this input uncertainty. Experimental results show that our embedding model achieves higher accuracy when retrieving similar poses across different camera views, in comparison with 2D-to-3D pose lifting models. We also demonstrate the effectiveness of applying our embeddings to view-invariant action recognition and video alignment. Our code is available at https://github.com/google-research/google-research/tree/master/poem. View details
    Naive-Student: Leveraging semi-supervised learning in video sequences for urban scene segmentation
    Liang-Chieh Chen
    Rapha Gontijo Lopes
    Bowen Cheng
    Maxwell D. Collins
    Barret Richard Zoph
    Jon Shlens
    European Conference on Computer Vision (ECCV) (2020)
    Preview abstract Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale, human annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. This limitation is particularly notable for image segmentation tasks where the expense of human annotation may be especially large, yet large amounts of unlabeled data may exist. In this work, we ask if we may leverage unlabeled video sequences to improve the performance on urban scene segmentation using semi-supervised learning. The goal of this work is to avoid the construction of sophisticated, learned architectures specific to label propagation (e.g., patch matching and optical flow). Instead, we simply predict pseudo-labels for the unlabeled data and train subsequent models with a mix of human-annotated and pseudo-labeled data. The procedure is iterated for several times. As a result, our model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks, reaching the performance of 67.6% PQ, 42.4% AP, and 85.1% mIOU on the test set. We view this work as a notable step for building a simple procedure to harness unlabeled video sequences to surpass state-of-the-art performance on core computer vision tasks. View details
    Preview abstract Understanding the degree to which human facial expressions co-vary with specific social contexts across cultures is central to the theory that emotions enable adaptive responses to important challenges and opportunities. Concrete evidence linking social context to specific facial expressions is sparse and is largely based on survey-based approaches, which are often constrained by language and small sample sizes. Here, by applying machine-learning methods to real-world, dynamic behaviour, we ascertain whether naturalistic social contexts (for example, weddings or sporting competitions) are associated with specific facial expressions across different cultures. In two experiments using deep neural networks, we examined the extent to which 16 types of facial expression occurred systematically in thousands of contexts in 6 million videos from 144 countries. We found that each kind of facial expression had distinct associations with a set of contexts that were 70% preserved across 12 world regions. Consistent with these associations, regions varied in how frequently different facial expressions were produced as a function of which contexts were most salient. Our results reveal fine-grained patterns in human facial expressions that are preserved across the modern world. View details
    Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
    Bharath Hariharan
    Claire Cardie
    Menglin Jia
    Mengyun Shi
    Serge Belongie
    Yin Cui
    (2020)
    Preview abstract In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask RCNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. We also demonstrate instance segmentation models pre-trained on Fashionpedia achieve better transfer learning performance on other fashion datasets than ImageNet pre-training. Fashionpedia is available at: https://fashionpedia.github.io/home/index.html. View details
    FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
    Yuning Chai
    Bastian Leibe
    Liang-chieh Chen
    International Conference on Computer Vision and Pattern Recognition (CVPR) (2019) (to appear)
    Preview abstract Recently, there has been a lot of progress for video object segmentation (VOS). However, many of the most successful methods are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we propose FEELVOS as a simple and fast method which does not rely on fine-tuning. In order to segment a video frame, FEELVOS uses a semantic pixel-wise embedding together with a global and a local matching mechanism to transfer information from the first frame and from the previous frame of the video to the current frame. In contrast to previous work, our embedding is only used as an internal guidance of a convolutional network. Our novel dynamic segmentation head allows us to train the network including the embedding end-to-end for the multiple object segmentation task. We achieve a new state of the art in video object segmentation without fine-tuning on the DAVIS 2017 validation set with a J&F measure of 69.0%. View details
    Geo-Aware Networks for Fine-Grained Recognition
    Grace Chu
    Brian Potetz
    Weijun Wang
    Andrew Howard
    Fernando Andres Brucher
    ICCV 2019
    Preview abstract Fine grained recognition distinguishes among categories with subtle visual differences. To help identify fine grained categories, other information besides images has been used. However, there has been little effort on using geolocation information to improve fine grained classification accuracy. Our contributions to this field are twofold. First, to the best of our knowledge, this is the first paper which systematically examined various ways of incorporating geolocation information to fine grained images classification - from geolocation priors, to post-processing, to feature modulation. Secondly, to overcome the situation where no fine grained dataset has complete geolocation information, we introduce, and will make public, two fine grained datasets with geolocation by providing complementary information to existing popular datasets - iNaturalist and YFCC100M. Results on these datasets show that, the best geo-aware network can achieve 8.9% top-1 accuracy increase on iNaturalist and 5.9% increase on YFCC100M, compared with image only models' results. In addition, for small image baseline models like Mobilenet V2, the best geo-aware network gives 12.6% higher top-1 accuracy than image only model, achieving even higher performance than Inception V3 models without geolocation. Our work gives incentives to use geolocation information to improve fine grained recognition for both server and on-device models. View details
    Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
    Liang-chieh Chen
    Maxwell Collins
    Yukun Zhu
    George Papandreou
    Barret Zoph
    Jonathon Shlens
    NeurIPS (2018)
    Preview abstract The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems. View details
    Preview abstract In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (\eg, atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models. View details
    The iNaturalist Species Classification and Detection Dataset
    Grant Van Horn
    Oisin Mac Aodha
    Yin Cui
    Alex Shepard
    Pietro Perona
    Serge Belongie
    CVPR (2018)
    Preview abstract Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current nonensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning. View details
    Preview abstract Recent progress in deep learning has been accompanied by a growing concern for whether models are fair for users, with equally good performance across different demographics. In computer vision research, such questions are relevant to face detection and the related task of face attribute detection, among others. We measure race and gender inclusion in the context of smiling detection, and introduce a method for improving smiling detection across demographic groups. Our method introduces several modifications over existing detection methods, leveraging twofold transfer learning to better model facial diversity. Results show that this technique improves accuracy against strong baselines for most demographic groups as well as overall. Our best-performing model defines a new state-of-the-art for smiling detection, reaching 91% on the Faces of the World dataset. The accompanying multi-head diversity classifier also defines a new state-of-the-art for gender classification, reaching 93.87% on the Faces of the World dataset. This research demonstrates the utility of modeling race and gender to improve a face attribute detection task, using a twofold transfer learning framework that allows for privacy towards individuals in a target dataset. View details
    Learning Unified Embedding for Apparel Recognition
    Yuan Li
    Bo Wu
    Xiao Zhang
    ICCV Computational Fashion Workshop, IEEE (2017)
    Preview abstract In apparel recognition, specialized models (e.g. models trained for a particular vertical like dresses) can signifi- cantly outperform general models (i.e. models that cover a wide range of verticals). Therefore, deep neural network models are often trained separately for different verticals (e.g. [7]). However, using specialized models for different verticals is not scalable and expensive to deploy. This paper addresses the problem of learning one unified embedding model for multiple object verticals (e.g. all apparel classes) without sacrificing accuracy. The problem is tackled from two aspects: training data and training difficulty. On the training data aspect, we figure out that for a single model trained with triplet loss, there is an accuracy sweet spot in terms of how many verticals are trained together. To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss. This new loss makes the training easier and make it possible for more efficient use of the feature space. The end result is a unified model which can achieve the same retrieval accuracy as a number of separate specialized models, while having the model complexity as one. The effectiveness of our approach is shown in experiments. View details
    Preview abstract We propose a simple but effective online ensemble tracking algorithm based on convolutional neural networks (CNNs). The proposed algorithm employs a CNN for target representation, which has a common convolutional layers but has multiple branches of fully connected layers. For better regularization, a subset of branches in the CNN are selected randomly for online learning whenever target appearance models need to be updated. We call this technique BranchOut. In addition, each branch may have a different number of layers conceptually to maintain variable abstraction levels of target appearances. BranchOut with multilevel target representation allows us to learn robust target appearance models with great diversity and makes it possible to handle various challenges related to target appearance modeling effectively. Our algorithm is tested standard tracking benchmarks and shows the state-of-the-art performance even without pretraining on tracking sequences. View details
    Large-Scale Object Classification Using Label Relation Graphs
    Jia Deng
    Yangqing Jia
    Andrea Frome
    Samy Bengio
    Yuan Li
    European Conference on Computer Vision (2014)
    Preview abstract . In this paper we study how to perform object classification in a principled way that exploits the rich structure of real world labels. We develop a new model that allows encoding of flexible relations between labels. We introduce Hierarchy and Exclusion (HEX) graphs, a new formalism that captures semantic relations between any two labels applied to the same object: mutual exclusion, overlap and subsumption. We then provide rigorous theoretical analysis that illustrates properties of HEX graphs such as consistency, equivalence, and computational implications of the graph structure. Next, we propose a probabilistic classification model based on HEX graphs and show that it enjoys a number of desirable properties. Finally, we evaluate our method using a large-scale benchmark. Empirical results demonstrate that our model can signifi- cantly improve object classification by exploiting the label relations. View details
    Tour the world: a technical demonstration of a web-scale landmark recognition engine
    Yan-Tao Zheng
    Ulrich Buddemeier
    Fernando Brucher
    Tat-Seng Chua
    MM '09: Proceedings of the seventeen ACM international conference on Multimedia, ACM, New York, NY, USA (2009), pp. 961-962
    Preview
    Tour the World: building a web-scale landmark recognition engine
    Yantao Zheng
    Ulrich Buddemeier
    Fernando Brucher
    Tat-Seng Chua
    International Conference on Computer Vision and Pattern Recognition (CVPR) (2009)
    Preview
    Large-scale Privacy Protection in Google Street View
    Andrea Frome
    German Cheung
    Ahmad Abdulkader
    Marco Zennaro
    Bo Wu
    Luc Vincent
    IEEE International Conference on Computer Vision (2009)
    Preview abstract The last two years have witnessed the introduction and rapid expansion of products based upon large, systematically-gathered, street-level image collections, such as Google Street View, EveryScape, and Mapjack. In the process of gathering images of public spaces, these projects also capture license plates, faces, and other information considered sensitive from a privacy standpoint. In this work, we present a system that addresses the challenge of automatically detecting and blurring faces and license plates for the purpose of privacy protection in Google Street View. Though some in the field would claim face detection is "solved", we show that state-of-the-art face detectors alone are not sufficient to achieve the recall desired for large-scale privacy protection. In this paper we present a system that combines a standard sliding-window detector tuned for a high recall, low-precision operating point with a fast post-processing stage that is able to remove additional false positives by incorporating domain-specific information not available to the sliding-window detector. Using a completely automatic system, we are able to sufficiently blur more than 89% of faces and 94-96% of license plates in evaluation sets sampled from Google Street View imagery. The full paper will appear from IEEE. View details
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