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Unified Visual-Semantic Embeddings: Bridging vision and Language with Structured Meaning Representations 32

Unified Visual-Semantic Embeddings: Bridging Vision and Lang...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Hao Mao, Jiayuan Zhang, Yufeng Jiang, Yuning Li, Lei Sun, Weiwei Ma, Wei-Ying Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Sch Econ Shanghai Peoples R China Fudan Univ Syst & Shanghai Key Lab Data Sci Shanghai Peoples R China Tsinghua Univ Shanghai Inst Intelligent Elect & Syst Beijing Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci ITCS Beijing Peoples R China Bytedance AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

We propose the Unified Visual-Semantic Embeddings (Unified VSE) for learningajoint space of visual representation and textual semantics. The model unifies the embeddings of concepts at different levels: objects, attributes,relations, andfull scenes. We view the sentential semantics as a combination of different semantic components such as objects and relations;their embeddings are aligned with different image regions. A contrastive learningapproachis proposedfor the effective learning of this fine-grained alignmentfrom only image-captionpairs. We also present a simple yet effective approach that enforces the coverage of caption embeddings on the semantic components that appearin the sentence. We demonstrate that the Unified VSE outperforms baselines on cross-modal retrievaltasks;the enforcement of the semantic coverage improves the model's robustness in defending text-domain adversarialattacks. Moreover our model empowers the use of visual cues to accurately resolve word dependencies in novel sentences.

关键词： Representation Learning vision + Language

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SignGraph: A Sign Sequence is Worth Graphs of Nodes

SignGraph: A Sign Sequence is Worth Graphs of Nodes

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

作者： Gan, Shiwei Yin, Yafeng Jiang, Zhiwei Wen, Hongkai Xie, Lei Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Warwick Dept Comp Sci Warwick England

ISBN: (纸本)9798350353006

Despite the recent success of sign language research, the widely adopted CNN-based backbones are mainly migrated from other computer vision tasks, in which the contours and texture of objects are crucial for identifying objects. They usually treat sign frames as grids and may fail to capture effective cross-region features. In fact, sign language tasks need to focus on the correlation of different regions in one frame and the interaction of different regions among adjacent frames for identifying a sign sequence. In this paper, we propose to represent a sign sequence as graphs and introduce a simple yet effective graph-based sign language processing architecture named SignGraph, to extract cross-region features at the graph level. SignGraph consists of two basic modules: Local Sign Graph (LSG) module for learning the correlation of intra-frame cross-region features in one frame and Temporal Sign Graph (TSG) module for tracking the interaction of inter- frame cross-region features among adjacent frames. With LSG and TSG, we build our model in a multiscale manner to ensure that the representation of nodes can capture cross-region features at different granularities. Extensive experiments on current public sign language datasets demonstrate the superiority of our SignGraph model. Our model achieves very competitive performances with the SOTA model, while not using any extra cues. Code and models are available at: https://***/gswycf/SignGraph.

关键词： Graph convolutional network Sign language Sign language recognition

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ViM: Out-Of-Distribution with Virtual-logit Matching

ViM: Out-Of-Distribution with Virtual-logit Matching

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

作者： Wang, Haoqi Li, Zhizhong Feng, Litong Zhang, Wayne SenseTime Res Hong Kong Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we propose a novel OOD scoring method named Virtual-logit Matching (ViM), which combines the class-agnostic score from feature space and the In-Distribution (ID) class-dependent logits. Specifically, an additional logit representing the virtual OOD class is generated from the residual of the feature against the principal space, and then matched with the original logits by a constant scaling. The probability of this virtual logit after softmax is the indicator of OOD-ness. To facilitate the evaluation of large-scale OOD detection in academia, we create a new OOD dataset for ImageNet-1K, which is human-annotated and is 8.8x the size of existing datasets. We conducted extensive experiments, including CNNs and vision transformers, to demonstrate the effectiveness of the proposed ViM score. In particular, using the BiT-S model, our method gets an average AUROC 90.91% on four difficult OOD benchmarks, which is 4% ahead of the best baseline. Code and dataset are available at https://***/haoqiwang/vim.

关键词： computer vision Codes Computational modeling computer architecture Benchmark testing Transformers Feature extraction

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M³: Multimodal Memory Modelling for Video Captioning 31

M<SUP>3</SUP>: Multimodal Memory Modelling for Video Caption...

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

作者： Wang, Junbo Wang, Wei Huang, Yan Wang, Liang Tan, Tieniu Ctr Res Intelligent Percept & Comp CRIPAC NLPR Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat CEBSIT Beijing Peoples R China UCAS Beijing Peoples R China

ISBN: (纸本)9781538664209

Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, video captioning has made great progress. However learning an effective mapping from the visual sequence space to the language space is still a challenging problem due to the long-term multimodal dependency modelling and semantic misalignment. Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems [35] and working memory is the key factor of visual attention [33], we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments. Specifically, similar to [10], the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. To evaluate the proposed model, we perform experiments on two public datasets: MSVD and MSR-VT. The experimental results demonstrate that our method outperforms most of the stateof- the-art methods in terms of BLEU and METEOR.

关键词： Visualization Decoding Feature extraction Computational modeling computer vision Task analysis Neural networks

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Learning Selective Self-Mutual Attention for RGB-D Saliency Detection

Learning Selective Self-Mutual Attention for RGB-D Saliency ...

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

作者： Liu, Nian Zhang, Ni Han, Junwei Northwestern Polytech Univ Xian Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728171685

Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of distribution gap or information loss. Some other models use the feature fusion scheme but are limited by the linear feature fusion methods. In this paper, we propose to fuse attention learned in both modalities. Inspired by the Non-local model, we integrate the self-attention and each other's attention to propagate long-range contextual dependencies, thus incorporating multi-modal information to learn attention and propagate contexts more accurately. Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term. We embed the proposed attention module in a two-stream CNN for RGB-D saliency detection. Furthermore, we also propose a residual fusion module to fuse the depth decoder features into the RGB stream. Experimental results on seven benchmark datasets demonstrate the effectiveness of the proposed model components and our final saliency model. Our code and saliency maps are available at https://***/nnizhang/S2MA.

关键词： computer vision

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CRRN: Multi-Scale Guided Concurrent Reflection Removal Network 31

CRRN: Multi-Scale Guided Concurrent Reflection Removal Netwo...

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

作者： Wan, Renjie Shi, Boxin Duan, Ling-Yu Tan, Ah-Hwee Kot, Alex C. Nanyang Technol Univ Interdisciplinary Grad Sch Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Peking Univ Natl Engn Lab Video Technol Sch EECS Beijing Peoples R China

ISBN: (纸本)9781538664209

Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.

关键词： Training Glass computer vision Feature extraction Cameras Training data

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Mesoscopic photogrammetry with an unstabilized phone camera

Mesoscopic photogrammetry with an unstabilized phone camera

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

作者： Zhou, Kevin C. Cooke, Colin Park, Jaehee Qian, Ruobing Horstmeyer, Roarke Izatt, Joseph A. Farsiu, Sina Duke Univ Durham NC 27708 USA

ISBN: (纸本)9781665445092

We present a feature free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-based approach jointly registers and stitches all the images by estimating a coaligned height map, which acts as a pixel-wise radial deformation field that orthorectifies each camera image to allow plane-plus-parallax registration. The height maps themselves are reparameterized as the output of an untrained encoder-decoder convolutional neural network (CNN) with the raw camera images as the input, which effectively removes many reconstruction artifacts. Our method also jointly estimates both the camera's dynamic 6D pose and its distortion using a nonparametric model, the latter of which is especially important in mesoscopic applications when using cameras not designed for imaging at short working distances, such as smartphone cameras. We also propose strategies for reducing computation time and memory, applicable to other multi frame registration problems. Finally, we demonstrate our method using sequences of multi-megapixel images captured by an unstabilized smartphone on a variety of samples (e.g., painting brushstrokes, circuit board, seeds).

关键词： computer vision Solid modeling Three-dimensional displays Computational modeling Tools Cameras Distortion

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One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching

One Loss for Quantization: Deep Hashing with Discrete Wasser...

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

作者： Doan, Khoa D. Yang, Peng Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. Hashing aims to learn a binary-output function that maps an image to a binary vector. For optimal retrieval performance, producing balanced hash codes with low-quantization error to bridge the gap between the learning stage's continuous relaxation and the inference stage's discrete quantization is important. However, in the existing deep supervised hashing methods, coding balance and low-quantization error are difficult to achieve and involve several losses. We argue that this is because the existing quantization approaches in these methods are heuristically constructed and not effective to achieve these objectives. This paper considers an alternative approach to learning the quantization constraints. The task of learning balanced codes with low quantization error is re-formulated as matching the learned distribution of the continuous codes to a pre-defined discrete, uniform distribution. This is equivalent to minimizing the distance between two distributions. We then propose a computationally efficient distributional distance by leveraging the discrete property of the hash functions. This distributional distance is a valid distance and enjoys lower time and sample complexities. The proposed single-loss quantization objective can be integrated into any existing supervised hashing method to improve code balance and quantization error. Experiments confirm that the proposed approach substantially improves the performance of several representative hashing methods.

关键词： Hash functions computer vision Quantization (signal) Codes Computational modeling Estimation Encoding

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A Framework for Learning Ante-hoc Explainable Models via Concepts

A Framework for Learning Ante-hoc Explainable Models via Con...

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

作者： Sarkar, Anirban Vijaykeerthy, Deepak Sarkar, Anindya Balasubramanian, Vineeth N. IIT Hyderabad Hyderabad Telangana India IBM Res Bangalore Karnataka India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and jointly trains the whole module that shows high predictive performance and generates meaningful explanations in terms of concepts. Our training strategy is suitable for unsupervised concept learning with much lesser parameter space requirements compared to baseline methods. Our proposed model also has provision for leveraging self-supervision on concepts to extract better explanations. However, with full concept supervision, we achieve the best predictive performance compared to recently proposed concept-based explainable models. We report both qualitative and quantitative results with our method, which shows better performance than recently proposed concept-based explainability methods. We reported exhaustive results with two datasets without ground truth concepts, i.e., CIFARIO, ImageNet, and two datasets with ground truth concepts, i.e., AwA2, CUB-200, to show the effectiveness of our method for both cases. To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet.

关键词： Training Deep learning Representation learning computer vision Ethics Pipelines Predictive models

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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