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Crossing the Gap: Domain Generalization for Image Captioning

Crossing the Gap: Domain Generalization for Image Captioning

引用

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

作者： Ren, Yuchen Mao, Zhendong Fang, Shancheng Lu, Yan He, Tong Du, Hao Zhang, Yongdong Ouyang, Wanli Univ Sci & Technol China Hefei Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing image captioning methods are under the assumption that the training and testing data are from the same domain or that the data from the target domain (i.e., the domain that testing data lie in) are accessible. However, this assumption is invalid in real-world applications where the data from the target domain is inaccessible. In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process. We first construct a benchmark dataset for DGIC, which helps us to investigate models' domain generalization (DG) ability on unseen domains. With the support of the new benchmark, we further propose a new framework called language-guided semantic metric learning (LSML) for the DGIC setting. Experiments on multiple datasets demonstrate the challenge of the task and the effectiveness of our newly proposed benchmark and LSML framework.

关键词： and reasoning language vision

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An Ensemble Approach for Facial Behavior Analysis in-the-wild Video

An Ensemble Approach for Facial Behavior Analysis in-the-wil...

引用

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

作者： Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper introduces the 3rd Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. We focused on solving the problem of the Valence-Arousal (VA) estimation and Action Unit (AU) detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features;the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with local attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model. The result achieved 0.450 for valence and 0.445 for arousal on the test set, outperforming the baseline method with a corresponding CCC of 0.180 for valence and 0.170 for arousal. We also performed additional experiments on the action unit task with simple transformer blocks. We achieved a score of 49.04 on the test set in terms of F-1 score, which outperforms the baseline method with a corresponding F1 score of 36.50. Our submission to ABAW3 2022 ranks 3rd for both tasks.

关键词： Human computer interaction Sentiment analysis Gold Estimation Logic gates Feature extraction Transformers

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SFD2: Semantic-guided Feature Detection and Description

SFD2: Semantic-guided Feature Detection and Description

引用

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

作者： Xue, Fei Budvytis, Ignas Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress unreliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day-Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. Code is available at https://***/feixue94/sfd2.

关键词： Low-level vision

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High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

引用

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

作者： Han, Jin Asano, Yuta Shi, Boxin Zheng, Yinqiang Sato, Imari Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Natl Inst Informat Tokyo Japan Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

关键词： Physics-based vision and shape-from-X

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AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

AMT: All-Pairs Multi-Field Transforms for Efficient Frame In...

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

作者： Li, Zhen Zhu, Zuo-Liang Han, Ling-Hao Hou, Qibin Guo, Chun-Le Cheng, Ming-Ming Nankai Univ VCIP CS Tianjin Peoples R China

ISBN: (纸本)9798350301298

We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://***/MCG-NKU/AMT.

关键词： Low-level vision

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Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Towards Automated Polyp Segmentation Using Weakly- and Semi-...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ren, Guangyu Lazarou, Michalis Yuan, Jing Stathaki, Tania Imperial College London United Kingdom

ISBN: (纸本)9798350302493

Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. To this end, we propose a novel weakly- and semi-supervised learning polyp segmentation framework that can be trained using only weakly annotated images along with unlabeled images making it very cost-efficient to use. More specifically our contributions are: 1) a novel weakly annotated polyp dataset, 2) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial *** experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning. Code can be found in https://***/ic-qialanqian/WS-DefSegNet. © 2023 ieee.

关键词： computer aided diagnosis

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Light Field Synthesis from a Monocular Image using Variable LDI

Light Field Synthesis from a Monocular Image using Variable ...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Bak, Junhyeong Kyu Park, In Inha University Department of Electrical and Computer Engineering Incheon22212 Korea Republic of

ISBN: (纸本)9798350302493

Recent advancements in learning-based novel view synthesis enable users to synthesize light field from a monocular image without special equipment. Moreover, the state-of-the-art techniques including multiplane image (MPI) show outstanding performance in synthesizing accurate light field from a monocular image. In this study, we propose a new variable layered depth image (VLDI) representation to generate precise light field synthesis results using only a few layers. Our method exploits LDI representation built on a new two-stream halfway fusion network and transformation process. This framework has an efficient structure that directly generates the region that does not require network prediction from inputs. As a result, the proposed method allows us to acquire high-quality light field easily and quickly. Experimental results show that the proposed method outperforms the previous works quantitatively and qualitatively for diverse examples. © 2023 ieee.

关键词： computer vision

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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Visibility Constrained Wide-band Illumination Spectrum Desig...

引用

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

作者： Niu, Muyao Li, Zhuoxiao Zhong, Zhihang Zheng, Yinqiang Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350301298

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://***/MyNiuuu/VCSD.

关键词： Physics-based vision and shape-from-X

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Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

Cali-NCE: Boosting Cross-modal Video Representation Learning...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhao, Nanxuan Jiao, Jianbo Xie, Weidi Lin, Dahua University of Bath Department of Computer Science United Kingdom University of Birmingham School of Computer Science United Kingdom Shanghai Jiaotong University Cooperative Medianet Innovation Center China Chinese University of Hong Kong Department of Information Engineering Hong Kong

ISBN: (纸本)9798350302493

With the large-scale video-text datasets being collected, learning general visual-textual representation has gained increasing attention. While recent methods are designed with the assumption that the alt-text description naturally conveys the meaning and context of the video in semantics (i.e. well aligned with each other), it is unlikely to be satisfied for the Internet data, which potentially harms the quality of the learned visual-textual representation. To address this challenge, we first revisit three mainstream approaches: correspondence modeling, contrastive learning and predictive coding, demonstrating that a simple co-training strategy with these methods leads to a clear improvement in performance. To further explore the complementary nature of different training strategies, we propose a simple yet effective joint training framework that factorizes the total objective into conditional ones, termed as Cali-NCE 1. Our method first estimates confidence scores for measuring the correspondence between video and text descriptions, and the scores are later used to calibrate the sample weightings during contrastive training. Through extensive experiments, we show that the proposed approach achieves state-of-the-art performance on multiple downstream tasks: text-to-video retrieval, video action recognition, and video retrieval. © 2023 ieee.

关键词： Semantics

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Leveraging per Image-Token Consistency for vision-Language Pre-training

Leveraging per Image-Token Consistency for Vision-Language P...

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

作者： Gou, Yunhao Ko, Tom Yang, Hansi Kwok, James Zhang, Yu Wang, Mingxuan Southern Univ Sci & Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China ByteDance Ai Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://***/gyhdog99/epic

关键词： Multi-modal learning

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