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Composing Text and Image for Image Retrieval - An Empirical Odyssey 32

Composing Text and Image for Image Retrieval - An Empirical ...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Vo, Nam Jiang, Lu Sun, Chen Murphy, Kevin Li, Li-Jia Fei-Fei, Li Hays, James Georgia Tech Atlanta GA 30332 USA Google AI Mountain View CA USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781728132938

In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar, but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we embed the query (reference image plus modification text) and the target (images). The encoding function of the image text query learns a representation, such that the similarity with the target image representation is high iff it is a "positive match". We propose a new way to combine image and text through residual connection, that is designed for this retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to perform image classification with compositionally novel labels, and we outperform previous methods on MIT-States on this task.

关键词： Categorization Datasets and Evaluation recognition: detection Representation Learning Retrieval Vision + Language

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Hybrid Task Cascade for Instance Segmentation 32

Hybrid Task Cascade for Instance Segmentation

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Chen, Kai Pang, Jiangmiao Wang, Jiaqi Xiong, Yu Li, Xiaoxiao Sun, Shuyang Feng, Wansen Liu, Ziwei Shi, Jianping Ouyang, Wanli Loy, Chen Change Lin, Dahua Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Zhejiang Univ Hangzhou Peoples R China Univ Sydney Sydney NSW Australia Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728132938

Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing;(2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object detection Task.

关键词： Categorization recognition: detection Retrieval

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Weakly Supervised Video Moment Retrieval From Text Queries 32

Weakly Supervised Video Moment Retrieval From Text Queries

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Mithun, Niluthpol Chowdhury Paul, Sujoy Roy-Chowdhury, Amit K. Univ Calif Riverside Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781728132938

There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.

关键词： Categorization recognition: detection Retrieval Vision + Language

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Meta-Transfer Learning for Few-Shot Learning 32

Meta-Transfer Learning for Few-Shot Learning

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Sun, Qianru Liu, Yaoyao Chua, Tat-Seng Schiele, Bernt Natl Univ Singapore Singapore Singapore Tianjin Univ Tianjin Peoples R China Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany NUS Singapore Singapore

ISBN: (纸本)9781728132938

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum for MTL. We conduct experiments using (5-class, 1-shot) and (5-class, 5-shot) recognition tasks on two challenging few-shot learning benchmarks: miniImageNet and Fewshot-CIFAR100. Extensive comparisons to related works validate that our meta-transfer learning approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy(1).

关键词： Categorization Deep Learning recognition: detection Retrieval

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Graph-Based Global Reasoning Networks 32

Graph-Based Global Reasoning Networks

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Chen, Yunpeng Rohrbach, Marcus Yan, Zhicheng Yan, Shuicheng Feng, Jiashi Kalantidis, Yannis Facebook AI Menlo Pk CA 94025 USA Natl Univ Singapore Singapore Singapore Qihoo 360 AI Inst Beijing Peoples R China

ISBN: (纸本)9781728132938

Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed. After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks. We further present a highly efficient instantiation of the proposed approach and introduce the Global Reasoning unit (GloRe unit) that implements the coordinate-interaction space mapping by weighted global pooling and weighted broadcasting, and the relation reasoning via graph convolution on a small graph in interaction space. The proposed GloRe unit is lightweight, end-to-end trainable and can be easily plugged into existing CNNs for a wide range of tasks. Extensive experiments show our GloRe unit can consistently boost the performance of state-of-the-art backbone architectures, including ResNet [15, 16], ResNeXt [34], SE-Net [18] and DPN [9], for both 2D and 3D CNNs, on image classification, semantic segmentation and video action recognition task.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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Destruction and Construction Learning for Fine-grained Image recognition 32

Destruction and Construction Learning for Fine-grained Image...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Chen, Yue Bai, Yalong Zhang, Wei Mei, Tao JD AI Res Beijing Peoples R China

ISBN: (纸本)9781728132938

Delicate feature representation about object parts plays a critical role in fine-grained recognition. For example, experts can even distinguish fine-grained objects relying only on object parts according to professional knowledge. In this paper we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge. Besides the standard classification backbone network, another "destruction and construction" stream is introduced to carefully "destruct" and then "reconstruct" the input image, for learning discriminative regions and features. More specifically,for "destruction", we first partition the input image into local regions and then shuffle them by a Region Confusion Mechanism (RCM). To correctly recognize these destructed images, the classification network has to pay more attention to discriminative regions for spotting the differences. To compensate the noises introduced by RCM, an adversarial loss, which distinguishes original images from destructed ones, is applied to reject noisy patterns introduced by RCM. For "construction ", a region alignment network, which tries to restore the original spatial layout of local regions, is followed to model the semantic correlation among local regions. By jointly training with parameter sharing, our proposed DCL injects more discriminative local details to the classification network. Experimental results show that our proposed framework achieves state-of-the-art performance on three standard benchmarks. Moreover, our proposed method does not need any external knowledge during training,and there is no computation overhead at inference time except the standard classification network feed-forwarding.

关键词： Categorization Deep Learning recognition: detection Retrieval

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ELASTIC: Improving CNNs with Dynamic Scaling Policies 32

ELASTIC: Improving CNNs with Dynamic Scaling Policies

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Huiyu Kembhavi, Aniruddha Farhadi, Ali Yuille, Alan Rastegari, Mohammad Johns Hopkins Univ Baltimore MD 21218 USA PRIOR Allen Inst AI Seattle WA USA Univ Washington Seattle WA 98195 USA Xnor Ai Seattle WA USA

ISBN: (纸本)9781728132938

Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We argue that the scaling policy should be learned from data. In this paper, we introduce Elastic, a simple, efficient and yet very effective approach to learn a dynamic scale policy from data. We formulate the scaling policy as a non-linear function inside the network's structure that (a) is learned from data, (b) is instance specific, (c) does not add extra computation, and (d) can be applied on any network architecture. We applied Elastic to several state-of-the-art network architectures and showed consistent improvement without extra (sometimes even lower) computation on ImageNet classification, MSCOCO multi-label classification, and PASCAL VOC semantic segmentation. Our results show major improvement for images with scale challenges.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images 32

Learning Cross-Modal Embeddings with Adversarial Networks fo...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Hao Sahoo, Doyen Liu, Chenghao Lim, Ee-peng Hoi, Steven C. H. Singapore Management Univ Singapore Singapore Salesforce Res Asia Singapore Singapore

ISBN: (纸本)9781728132938

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to learn a common embedding feature space between the two modalities, in which our approach consists of several novel ideas: (i) learning by using a new triplet loss scheme together with an effective sampling strategy, (ii) imposing modality alignment using an adversarial learning strategy, and (iii) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality. ACME achieves the state-of-the-art performance on the benchmark Recipe1M dataset, validating the efficacy of the proposed technique.

关键词： Big Data Biological and Cell Microscopy Categorization Large Scale Methods Medical Others recognition: detection Retrieval

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Adaptively Connected Neural Networks 32

Adaptively Connected Neural Networks

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Guangrun Wang, Keze Lin, Liang Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781728132938

This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) in two aspects. First, ACNet employs a flexible way to switch global and local inference in processing the internal feature representations by adaptively determining the connection status among the feature nodes (e.g., pixels of the feature maps)(1). We can show that existing CNNs, the classical multilayer perceptron (MLP), and the recently proposed non-local network (NLN) [48] are all special cases of ACNet. Second, ACNet is also capable of handling non-Euclidean data. Extensive experimental analyses on a variety of benchmarks (i.e., ImageNet-1k classification, COCO 2017 detection and segmentation, CUHK03 person re-identification, CIFAR analysis, and Cora document categorization) demonstrate that ACNet cannot only achieve state-of-the-art performance but also overcome the limitation of the conventional MLP and CNN2. The code is available at https: //***/wanggrun/Adaptively-Connected-Neural-Networks.

关键词： Categorization recognition: detection Representation Learning Retrieval

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Learning to Learn from Noisy Labeled Data 32

Learning to Learn from Noisy Labeled Data

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Li, Junnan Wong, Yongkang Zhao, Qi Kankanhalli, Mohan S. Natl Univ Singapore Singapore Singapore Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781728132938

Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate labels. Training on noisy labeled datasets causes performance degradation because DNNs can easily overfit to the label noise. To overcome this problem, we propose a noise-tolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update. The proposed meta-learning method simulates actual training by generating synthetic noisy labels, and train the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the Clothing 1M dataset. The results demonstrate the advantageous performance of the proposed method compared to state-of-the-art baselines.

关键词： Categorization Deep Learning recognition: detection Retrieval

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