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HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

HGFormer: Hierarchical Grouping Transformer for Domain Gener...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jian Ding Nan Xue Gui-Song Xia Bernt Schiele Dengxin Dai NERCMS School of Computer Science Wuhan University China State Key Lab. LIESMARS Wuhan University China Max Planck Institute for Informatics Saarland Informatics Campus Germany

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain. Existing works show that vision Transformers are more robust than CNNs and show that this is related to the visual grouping property of self-attention. In this work, we propose a novel hierarchical grouping transformer (HGFormer) to explicitly group pixels to form part-level masks and then whole-level masks. The masks at different scales aim to segment out both parts and a whole of classes. HGFormer combines mask classification results at both scales for class label prediction. We assemble multiple interesting cross-domain settings by using seven public semantic segmentation datasets. Experiments show that HGFormer yields more robust semantic segmentation results than per-pixel classification methods and flat-grouping transformers, and outperforms previous methods significantly. Code will be available at https://***/dingjianswl0l/HGFormer.

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Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding

Hierarchical Semantic Correspondence Networks for Video Para...

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conference on computer vision and pattern recognition (CVPR)

作者： Chaolei Tan Zihang Lin Jian-Fang Hu Wei-Shi Zheng Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS.

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Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Raphaë l Jean Pierre-Luc St-Charles Sö ren Pirk Simon Brodeur Menya Solutions Mila Mila AMLRT Google Research

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

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MAGVLT: Masked Generative vision-and-Language Transformer

MAGVLT: Masked Generative Vision-and-Language Transformer

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conference on computer vision and pattern recognition (CVPR)

作者： Sungwoong Kim Daejin Jo Donghoon Lee Jongmin Kim Department of Artificial Intelligence Korea University Seoul South Korea Kakao Brain Seongnam South Korea

While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unified generative vision-and-language (VL) model that can produce both images and text sequences. Especially, we propose a generative VL transformer based on the non-autoregressive mask prediction, named MAGVLT, and compare it with an autoregressive generative VL transformer (ARGVLT). In comparison to ARGVLT, the proposed MAGVLT enables bidirectional context encoding, fast decoding by parallel token predictions in an iterative refinement, and extended editing capabilities such as image and text infilling. For rigorous training of our MAGVLT with image-text pairs from scratch, we combine the image-to-text, text-to-image, and joint image-and-text mask prediction tasks. Moreover, we devise two additional tasks based on the step-unrolled mask prediction and the selective prediction on the mixture of two image-text pairs. Experimental results on various downstream generation tasks of VL benchmarks show that our MAGVLT outperforms ARGVLT by a large margin even with significant inference speedup. Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderate-sized model (fewer than 500M parameters) even without the use of monomodal data and networks.

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TopNet: Transformer-Based Object Placement Network for Image Compositing

TopNet: Transformer-Based Object Placement Network for Image...

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conference on computer vision and pattern recognition (CVPR)

作者： Sijie Zhu Zhe Lin Scott Cohen Jason Kuen Zhifei Zhang Chen Chen Center for Research in Computer Vision University of Central Florida Adobe Research

We investigate the problem of automatically placing an object into a background image for image compositing. Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing. The quality of the composite image highly depends on the predicted location/scale. Existing works either generate candidate bounding boxes or apply sliding-window search using global representations from background and object images, which fail to model local information in background images. However, local clues in background images are important to determine the compatibility of placing the objects with certain locations/scales. In this paper, we propose to learn the correlation between object features and all local background features with a transformer module so that detailed information can be provided on all possible location/scale configurations. A sparse contrastive loss is further proposed to train our model with sparse supervision. Our new formulation generates a 3D heatmap indicating the plausibility of all location/scale combinations in one network forward pass, which is > 10 x faster than the previous sliding-window method. It also supports interactive search when users provide a pre-defined location or scale. The proposed method can be trained with explicit annotation or in a self-supervised manner using an off-the-shelf inpainting model, and it outperforms state-of-the-art methods significantly. User study shows that the trained model generalizes well to real-world images with diverse challenging scenes and object categories.

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Joint Learning of Blind Video Denoising and Optical Flow Estimation

Joint Learning of Blind Video Denoising and Optical Flow Est...

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

作者： Yu, Songhyun Park, Bumjun Park, Junwoo Jeong, Jechang Hanyang Univ Seoul South Korea Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Many deep-learning-based image/video denoising models have been developed, and recently, several approaches for training a denoising neural network without using clean images have been proposed. However, Noise2Noise method requires paired noisy data, and obtaining them is occasionally difficult, whereas other existing models trained using unpaired noisy data deliver limited performance. Obtaining an accurate optical flow from noisy videos is also a difficult task because conventional optical flow estimation methods are primarily focused on estimating the optical flow using clean videos. This study proposes a new framework to fine-tune video denoising and optical flow estimation networks using unpaired noisy videos. These two networks are jointly trained to realize synergy;an improvement in the denoising performance increases the accuracy of the flow estimation, and an improvement in the flow-estimation performance enhances the quality of the training data for the denoiser. Our experimental results reveal that proposed approach outperforms the existing training schemes in video denoising and also provides accurate optical flows even when the videos contain a considerable amount of noise.

关键词： Videos Noise reduction Optical flow Noise measurement Estimation Training computer vision

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Improving the Transferability of Adversarial Samples by Path-Augmented Method

Improving the Transferability of Adversarial Samples by Path...

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conference on computer vision and pattern recognition (CVPR)

作者： Jianping Zhang Jen-tse Huang Wenxuan Wang Yichen Li Weibin Wu Xiaosen Wang Yuxin Su Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong School of Software Engineering Sun Yat-sen University Huawei Singular Security Lab Beijing China

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates.

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Hard Patches Mining for Masked Image Modeling

Hard Patches Mining for Masked Image Modeling

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conference on computer vision and pattern recognition (CVPR)

作者： Haochen Wang Kaiyou Song Junsong Fan Yuxi Wang Jin Xie Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Megvii Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Science

Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pretraining task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct. 1 1 Code: https://***/Haochen-wang409/HPM

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Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation

Text-Guided Unsupervised Latent Transformation for Multi-Att...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiwen Wei Zhen Xu Cheng Liu Si Wu Zhiwen Yu Hau San Wong School of Computer Science and Engineering South China University of Technology Department of Computer Science Shantou University Peng Cheng Laboratory Department of Computer Science City University of Hong Kong

Great progress has been made in StyleGAN-based image editing. To associate with preset attributes, most existing approaches focus on supervised learning for semantically meaningful latent space traversal directions, and each manipulation step is typically determined for an individual attribute. To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image. Specifically, we adopt a two-stage architecture for a latent mapping network to break down the transformation process into two manageable steps. Our network first learns a diverse set of semantic directions tailored to an input image, and later nonlinearly fuses the ones associated with the target attributes to infer a residual vector. The resulting tightly interlinked two-stage architecture delivers the flexibility to handle diverse attribute combinations. By leveraging the cross-modal text-image representation of CLIP, we can perform pseudo annotations based on the semantic similarity between preset attribute text descriptions and training images, and further jointly train an auxiliary attribute classifier with the latent mapping network to provide semantic guidance. We perform extensive experiments to demonstrate that the adopted strategies contribute to the superior performance of TUSLT.

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Distilling vision-Language Pre-Training to Collaborate with Weakly-Supervised Temporal Action Localization

Distilling Vision-Language Pre-Training to Collaborate with ...

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conference on computer vision and pattern recognition (CVPR)

作者： Chen Ju Kunhao Zheng Jinxiang Liu Peisen Zhao Ya Zhang Jianlong Chang Qi Tian Yanfeng Wang CMIC Shanghai Jiao Tong University Huawei Cloud Shanghai AI Laboratory

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between classification and localization, make temporally localized results suffer from the serious incomplete issue. To tackle this issue without additional annotations, this paper considers to distill free action knowledge from vision-Language Pre-training (VLP), as we surprisingly observe that the localization results of vanilla VLP have an over-complete issue, which is just complementary to the CBP results. To fuse such complementarity, we propose a novel distillation-collaboration framework with two branches acting as CBP and VLP respectively. The framework is optimized through a dual-branch alternate training strategy. Specifically, during the B step, we distill the confident background pseudo-labels from the CBP branch; while during the F step, the confident foreground pseudo-labels are distilled from the VLP branch. As a result, the dualbranch complementarity is effectively fused to promote one strong alliance. Extensive experiments and ablation studies on THUMOS14 and ActivityNet1.2 reveal that our method significantly outperforms state-of-the-art methods.

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