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Prefix Conditioning Unifies Language and Label Supervision

Prefix Conditioning Unifies Language and Label Supervision

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

作者： Saito, Kuniaki Sohn, Kihyuk Zhang, Xiang Li, Chun-Liang Lee, Chen-Yu Saenko, Kate Pfister, Tomas Boston Univ Boston MA 02215 USA Google Cloud AI Res Mountain View CA USA Google Res Mountain View CA USA MIT IBM Watson Iab Cambridge MA USA

ISBN: (纸本)9798350301298

Pretraining visual models on web-scale image-caption datasets has recently emerged as a powerful alternative to traditional pretraining on image classification data. Image-caption datasets are more "open-domain", containing broader scene types and vocabulary words, and result in models that have strong performance in few- and zero-shot recognition tasks. However large-scale classification datasets can provide fine-grained categories with a balanced label distribution. In this work, we study a pretraining strategy that uses both classification and caption datasets to unite their complementary benefits. First, we show that naively unifying the datasets results in suboptimal performance in downstream zero-shot recognition tasks, as the model is affected by dataset bias: the coverage of image domains and vocabulary words is different in each dataset. We address this problem with novel Prefix Conditioning, a simple yet effective method that helps disentangle dataset biases from visual concepts. This is done by introducing prefix tokens that inform the language encoder of the input data type (e.g., classification vs caption) at training time. Our approach allows the language encoder to learn from both datasets while also tailoring feature extraction to each dataset. Prefix conditioning is generic and can be easily integrated into existing VL pretraining objectives, such as CLIP or UniCL. In experiments, we show that it improves zero-shot image recognition and robustness to image-level distribution shift.

关键词： language reasoning vision

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SelfD: Self-Learning Large-Scale Driving Policies From the Web

SelfD: Self-Learning Large-Scale Driving Policies From the W...

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

作者： Zhang, Jimuyang Zhu, Ruizhao Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on the internet can advance generalized intelligent systems, i.e., to robustly scale across perspectives, platforms, environmental conditions, scenarios, and geographical locations. However, it is difficult to directly leverage such large amounts of unlabeled and highly diverse data for complex 3D reasoning and planning tasks. Consequently, researchers have primarily focused on its use for various auxiliary pixel- and imagelevel computer vision tasks that do not consider an ultimate navigational objective. In this work, we introduce SelfD, a framework for learning scalable driving by utilizing large amounts of online monocular images. Our key idea is to leverage iterative semi-supervised training when learning imitative agents from unlabeled data. lb handle unconstrained viewpoints, scenes, and camera parameters, we train an image-based model that directly learns to plan in the Bird's Eye View (BEV) space. Next, we use unlabeled data to augment the decision-making knowledge and robustness of an initially trained model via self-training. In particular;we propose a pseudo-labeling step which enables making MI use of highly diverse demonstration data through "hypothetical" planning-based data augmentation. We employ a large dataset of publicly available YouTube videos to train Sea) and comprehensively analyze its generalization benefits across challenging navigation scenarios. Without requiring any additional data collection or annotation efforts, SelfD demonstrates consistent improvements (by up to 24%) in driving performance evaluation on nuScenes, Argoverse, Waymo, and CARLA.

关键词： Training computer vision Three-dimensional displays Navigation Annotations Data collection Cameras

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Visual-Semantic Matching by Exploring High-Order Attention and Distraction

Visual-Semantic Matching by Exploring High-Order Attention a...

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

作者： Li, Yongzhi Zhang, Duo Mu, Yadong Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ EECS Beijing Peoples R China Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781728171685

Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information (e.g., object-predicate-subject triplet, object-attribute pair) and visual distraction (i.e., despite the high relevance to textual query, images may also contain many prominent distracting objects or visual relations). Specifically, we build scene graphs for both visual and textual modalities. Our technical contributions are two-folds: firstly, we formulate the visual-semantic matching task as an attention-driven cross-modality scene graph matching problem. Graph convolutional networks (GCNs) are used to extract high-order information from two scene graphs. A novel cross-graph attention mechanism is proposed to contextually reweigh graph elements and calculate the inter-graph similarity;Secondly, some top-ranked samples are indeed false matching due to the co-occurrence of both highly-relevant and distracting information. We devise an information-theoretic measure for estimating semantic distraction and re-ranking the initial retrieval results. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the superiority of the proposed method and the effectiveness of both high-order attention and distraction.

关键词： computer vision

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Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects 27

Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zia, M. Zeeshan Stark, Michael Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat Munich Germany Imperial Coll London Robot Vis London England

ISBN: (纸本)9781479951178

Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes. While these representations have proven robust in a variety of applications, they provide only coarse approximations to the true 2D and 3D extent of objects. As a result, object-object interactions, such as occlusions or ground-plane contact, can be represented only superficially. In this paper, we approach the problem of scene understanding from the perspective of 3D shape modeling, and design a 3D scene representation that reasons jointly about the 3D shape of multiple objects. This representation allows to express 3D geometry and occlusion on the fine detail level of individual vertices of 3D wireframe models, and makes it possible to treat dependencies between objects, such as occlusion reasoning, in a deterministic way. In our experiments, we demonstrate the benefit of jointly estimating the 3D shape of multiple objects in a scene over working with coarse boxes, on the recently proposed KITTI dataset of realistic street scenes.

关键词： 3D object recognition Scene understanding 3D shape Occlusion object recognition Boxes Three Dimensions

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Iterative Visual Reasoning Beyond Convolutions 31

Iterative Visual Reasoning Beyond Convolutions

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Xinlei Li, Li-Jia Li Fei-Fei Gupta, Abhinav Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781538664209

We present a novel framework for iterative visual reasoning. Our framework goes beyond current recognition systems that lack the capability to reason beyond stack of convolutions. The framework consists of two core modules: a local module that uses spatial memory [4] to store previous beliefs with parallel updates;and a global graph-reasoning module. Our graph module has three components: a) a knowledge graph where we represent classes as nodes and build edges to encode different types of semantic relationships between them;b) a region graph of the current image where regions in the image are nodes and spatial relationships between these regions are edges;c) an assignment graph that assigns regions to classes. Both the local module and the global module roll-out iteratively and cross-feed predictions to each other to refine estimates. The final predictions are made by combining the best of both modules with an attention mechanism. We show strong performance over plain ConvNets, e.g. achieving an 8.4% absolute improvement on ADE [55] measured by per-class average precision. Analysis also shows that the framework is resilient to missing regions for reasoning.

关键词： Cognition Semantics Visualization Image edge detection Knowledge based systems computer architecture Automobiles

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Deep Unlearning via Randomized Conditionally Independent Hessians

Deep Unlearning via Randomized Conditionally Independent Hes...

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

作者： Mehta, Ronak Pal, Sourav Singh, Vikas Ravi, Sathya N. Univ Wisconsin Madison WI 53706 USA Univ Illinois Chicago IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent legislation has led to interest in machine unlearning, i.e., removing specific training samples from a predictive model as if they never existed in the training dataset. Unlearning may also be required due to corrupted/adversarial data or simply a user's updated privacy requirement. For models which require no training (k-NN), simply deleting the closest original sample can be effective. But this idea is inapplicable to models which learn richer representations. Recent ideas leveraging optimization-based updates scale poorly with the model dimension d, due to inverting the Hessian of the loss function. We use a variant of a new conditional independence coefficient, L-CODEC, to identify a subset of the model parameters with the most semantic overlap on an individual sample level. Our approach completely avoids the need to invert a (possibly) huge matrix. By utilizing a Markov blanket selection, we premise that L-CODEC is also suitable for deep unlearning, as well as other applications in vision. Compared to alternatives, L-CODEC makes approximate unlearning possible in settings that would otherwise be infeasible, including vision models used for face recognition, person re-identification and NLP models that may require unlearning samples identified for exclusion. Code is available at https://***/vsingh-group/LCODEC-deep-unlearning

关键词： Training Law Computational modeling Face recognition Semantics Legislation Predictive models

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Context Modeling in 3D Human Pose Estimation: A Unified Perspective

Context Modeling in 3D Human Pose Estimation: A Unified Pers...

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

作者： Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Ci, Hai Wang, Yizhou Peking Univ Ctr Frontiers Comp Studies Dept Comp Sci Beijing Peoples R China Peking Univ Ctr Data Sci Adv Inst Info Tech Beijing Peoples R China Beijing Film Acad Adv Innovat Ctr Future Visual Entertainment AICFV Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance.

关键词： Training computer vision Three-dimensional displays Pose estimation Benchmark testing Graph neural networks pattern recognition

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Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

Learning View-Disentangled Human Pose Representation by Cont...

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

作者： Zhao, Long Wang, Yuxiao Zhao, Jiaping Yuan, Liangzhe Sun, Jennifer J. Schroff, Florian Adam, Hartwig Peng, Xi Metaxas, Dimitris Liu, Ting Rutgers State Univ New Brunswick NJ 08901 USA Google Res Mountain View CA USA CALTECH Pasadena CA 91125 USA Univ Delaware Newark DE 19716 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

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://***/google-research/google-research/tree/master/poem.

关键词： computer vision Codes Training data Benchmark testing Data models pattern recognition Task analysis

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recognition BY USING AN ACTIVE SPACE-VARIANT SENSOR

RECOGNITION BY USING AN ACTIVE SPACE-VARIANT SENSOR

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1994 ieee computer-Society conference on computer vision and pattern recognition

作者： TISTARELLI, M UNIV GENOA DISTINTEGRATED ADV ROBOT LABI-16145 GENOAITALY

ISBN: (纸本)0818658258

The problem of object recognition is addressed. In the literature this task has been generally considered in a 'passive' perspective, where everything is static and there is no definite relation between the object and its environment. We propose an 'active' approach for object recognition, based on the capability of the observer to move and give a better description of the object under consideration and also to take advantage of the relations between the objects and the environment. This can be accomplished at the task level and at the sensor level. The face recognition problem, based on the face-space approach, is considered to demonstrate the advantage of adopting an active retina to sample the face, build a database and perform the recognition task. By using an active space-variant retina the size of the database is considerably reduced and consequently also the processing time for recognition. A comparative experiment using the active and static approach is presented.

关键词： pattern recognition

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CosFace: Large Margin Cosine Loss for Deep Face recognition 31

CosFace: Large Margin Cosine Loss for Deep Face Recognition

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Hao Wang, Yitong Zhou, Zheng Ji, Xing Gong, Dihong Zhou, Jingchao Li, Zhifeng Liu, Wei Tencent AI Lab Bellevue WA 98004 USA

ISBN: (纸本)9781538664209

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.

关键词： Face Face recognition Testing Mars Training Feature extraction Task analysis

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