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SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining

SyncMask: Synchronized Attentional Masking for Fashion-centr...

引用

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

作者： Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea

ISBN: (纸本)9798350353006

vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we pro-pose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.

关键词： Visual languages

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RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation 30

RefineNet: Multi-Path Refinement Networks for High-Resolutio...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lin, Guosheng Milan, Anton Shen, Chunhua Reid, Ian Nanyang Technol Univ Singapore Singapore Univ Adelaide Adelaide SA Australia Australian Ctr Robot Vis Brisbane Qld Australia

ISBN: (纸本)9781538604571

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new stateof- the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

关键词： multi-path pooling Semantics image resolution object recognition Refining segmentation Segmentation Identity mapping RESIDUAL

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Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

Multi-Source Domain Adaptation with Collaborative Learning f...

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

作者： He, Jianzhong Jia, Xu Chen, Shuaijun Liu, Jianzhuang Huawei Cloud Data Storage & Intelligent Vis Tech Res Dept Shenzhen Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781665445092

Multi-source unsupervised domain adaptation (MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap between source domains and target domain to some extent. Then, to fully exploit the essential semantic information across source domains, we propose a collaborative learning method for domain adaptation without seeing any data from target domain. In addition, similar to the setting of unsupervised domain adaptation, unlabeled target domain data is leveraged to further improve the performance of domain adaptation. This is achieved by additionally constraining the outputs of multiple adaptation models with pseudo labels online generated by an ensembled model. Extensive experiments and ablation studies are conducted on the widely-used domain adaptation benchmark datasets in semantic segmentation. Our proposed method achieves 59.0% mIoU on the validation set of Cityscapes by training on the labeled Synscapes and GTA5 datasets and unlabeled training set of Cityscapes. It significantly outperforms all previous state-of-the-arts single-source and multi-source unsupervised domain adaptation methods.

关键词： Training Image segmentation Adaptation models computer vision Semantics Benchmark testing Collaborative work

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Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way

Segment, Magnify and Reiterate: Detecting Camouflaged Object...

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

作者： Jia, Qi Yao, Shuilian Liu, Yu Fan, Xin Liu, Risheng Luo, Zhongxuan Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It is challenging to accurately detect camouflaged objects from their highly similar surroundings. Existing methods mainly leverage a single-stage detection fashion, while neglecting small objects with low-resolution fine edges requires more operations than the larger ones. To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion. Specifically, we design a new discriminative mask which makes the model attend on the fixation and edge regions. In addition, we leverage an attention-based sampler to magnify the object region progressively with no need of enlarging the image size. Extensive experiments show our SegMaR achieves remarkable and consistent improvements over other stateof-the-art methods. Especially, we surpass two competitive methods 7.4% and 20.0% respectively in average over standard evaluation metrics on small camouflaged objects. Additional studies provide more promising insights into SegMaR, including its effectiveness on the discriminative mask and its generalization to other network architectures. Code is available at https://***/dlut-dimt/SegMaR.

关键词： Measurement Image segmentation computer vision Codes Image edge detection Object detection Network architecture

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BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

BatchFormer: Learning to Explore Sample Relationships for Ro...

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

作者： Hou, Zhi Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in a vanilla way (i.e., from the perspectives of either the input or the loss function), failing to explore the internal structure of deep neural networks for learning with sample relationships. Inspired by this, we propose to enable deep neural networks themselves with the ability to learn the sample relationships from each mini-batch. Specifically, we introduce a batch transformer module or BatchFormer, which is then applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. By doing this, the proposed method enables the collaboration of different samples, e.g., the head-class samples can also contribute to the learning of the tail classes for long-tailed recognition. Furthermore, to mitigate the gap between training and testing, we share the classifier between with or without the BatchFormer during training, which can thus be removed during testing. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning. Code is made publicly available at https://***/zhihou7/BatchFormer.

关键词： Training Deep learning Representation learning computer vision Neural networks Tail Transformers

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SaCo Loss: Sample-wise Affinity Consistency for vision-Language Pre-training

SaCo Loss: Sample-wise Affinity Consistency for Vision-Langu...

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

作者： Wu, Sitong Tan, Haoru Tian, Zhuotao Chen, Yukang Qi, Xiaojuan Jia, Jiaya CUHK Hong Kong Peoples R China HKU Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350353006

vision-language pre-training (VLP) aims to learn joint representations of vision and language modalities. The contrastive paradigm is currently dominant in this field. However, we observe a notable misalignment phenomenon, that is, the affinity between samples has an obvious disparity across different modalities, namely "Affinity Inconsistency Problem". Our intuition is that, for a well-aligned model, two images that look similar to each other should have the same level of similarity as their corresponding texts that describe them. In this paper, we first investigate the reason of this inconsistency problem. We discover that the lack of consideration for sample-wise affinity consistency across modalities in existing training objectives is the central cause. To address this problem, we propose a novel loss function, named Sample-wise affinity Consistency (SaCo) loss, which is designed to enhance such consistency by minimizing the distance between image embedding similarity and text embedding similarity for any two samples. Our SaCo loss can be easily incorporated into existing vision-language models as an additional loss due to its complementarity for most training objectives. In addition, considering that pre-training from scratch is computationally expensive, we also provide a more efficient way to continuously pre-train on a converged model by integrating our loss. Experimentally, the model trained with our SaCo loss significantly outperforms the baseline on a variety of vision and language tasks.

关键词： Affinity Consistency vision-Language Pre-training

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Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis

Semantic-shape Adaptive Feature Modulation for Semantic Imag...

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

作者： Lv, Zhengyao Li, Xiaoming Niu, Zhenxing Cao, Bing Zuo, Wangmeng Tomorrow Adv Life Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Alibaba Grp Machine Intelligence Lab Hangzhou Peoples R China Tianjin Univ Tianjin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent years have witnessed substantial progress in semantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previous methods focus on exploiting the given semantic map, which just captures an object-level layout for an image. Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape. In order to exploit the part-level layouts, we propose a Shape-aware Position Descriptor (SPD) to describe each pixel's positional feature, where object shape is explicitly encoded into the SPD feature. Furthermore, a Semantic-shape Adaptive Feature Modulation (SAFM) block is proposed to combine the given semantic map and our positional features to produce adaptively modulated features. Extensive experiments demonstrate that the proposed SPD and SAFM significantly improve the generation of objects with rich details. Moreover, our method performs favorably against the SOTA methods in terms of quantitative and qualitative evaluation. The source code and model are available at SAFM.

关键词： computer vision Adaptation models Codes Shape Image synthesis Convolution Semantics

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Feature Transfer Learning for Face recognition with Under-Represented Data 32

Feature Transfer Learning for Face Recognition with Under-Re...

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

作者： Yin, Xi Yu, Xiang Sohn, Kihyuk Liu, Xiaoming Chandraker, Manmohan Michigan State Univ E Lansing MI 48824 USA NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781728132938

Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.

关键词： And Body Pose Biometrics Face Gesture

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Deep Rectangling for Image Stitching: A Learning Baseline

Deep Rectangling for Image Stitching: A Learning Baseline

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

作者： Nie, Lang Lin, Chunyu Liao, Kang Liu, Shuaicheng Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangular images can be generated by warping stitched images. However, these solutions only work for images with rich linear structures, leading to noticeable distortions for portraits and landscapes with non-linear objects. In this paper, we address these issues by proposing the first deep learning solution to image rectangling. Concretely, we predefine a rigid target mesh and only estimate an initial mesh to form the mesh deformation, contributing to a compact one-stage solution. The initial mesh is predicted using a fully convolutional network with a residual progressive regression strategy. To obtain results with high content fidelity, a comprehensive objective function is proposed to simultaneously encourage the boundary rectangular, mesh shape-preserving, and content perceptually natural. Besides, we build the first image stitching rectangling dataset with a large diversity in irregular boundaries and scenes. Experiments demonstrate our superiority over traditional methods both quantitatively and qualitatively.

关键词： Deep learning computer vision Nonlinear distortion Search problems Linear programming pattern recognition Computational efficiency

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RelTransformer: A Transformer-Based Long-Tail Visual Relationship recognition

RelTransformer: A Transformer-Based Long-Tail Visual Relatio...

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

作者： Chen, Jun Agarwal, Aniket Abdelkarim, Sherif Zhu, Deyao Elhoseiny, Mohamed King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Indian Inst Technol Chennai Tamil Nadu India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. These relationships typically have a long-tail distribution due to their compositional nature. This problem gets more severe when the vocabulary becomes large, rendering this task very challenging. This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. The method, called RelTransformer, represents each image as a fully-connected scene graph and restructures the whole scene into the relation-triplet and global-scene contexts. It directly passes the message from each element in the relation-triplet and global-scene contexts to the target relation via self-attention. We also design a learnable memory to augment the long-tail relation representation learning. Through extensive experiments, we find that our model generalizes well on many VRR benchmarks. Our model outperforms the best-performing models on two large-scale long-tail VRR benchmarks, VG8K-LT (+2.0% overall acc) and GQA-LT (+26.0% overall acc), both having a highly skewed distribution towards the tail. It also achieves strong results on the VG200 relation detection task.

关键词： Representation learning Visualization Vocabulary Image recognition Tail Benchmark testing Transformers

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