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MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining

MaskCLIP: Masked Self-Distillation Advances Contrastive Lang...

引用

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

作者： Dong, Xiaoyi Bao, Jianmin Zheng, Yinglin Zhang, Ting Chen, Dongdong Yang, Hao Zeng, Ming Zhang, Weiming Yuan, Lu Chen, Dong Wen, Fang Yu, Nenghai Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China Xiamen Univ Xiamen Peoples R China Microsoft Cloud AI Orlando FL USA

ISBN: (纸本)9798350301298

This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core idea of masked self-distillation is to distill representation from a full image to the representation predicted from a masked image. Such incorporation enjoys two vital benefits. First, masked self-distillation targets local patch representation learning, which is complementary to vision-language contrastive focusing on text-related representation. Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language. We provide specially designed experiments with a comprehensive analysis to validate the two benefits. Symmetrically, we also introduce the local semantic supervision into the text branch, which further improves the pretraining performance. With extensive experiments, we show that MaskCLIP, when applied to various challenging downstream tasks, achieves superior results in linear probing, finetuning, and zero-shot performance with the guidance of the language encoder. Code will be release at https://***/LightDXY/MaskCLIP.

关键词： detection recognition: Categorization retrieval

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KERM: Knowledge Enhanced Reasoning for vision-and-Language Navigation

KERM: Knowledge Enhanced Reasoning for Vision-and-Language N...

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

作者： Li, Xiangyang Wang, Zihan Yang, Jiahao Wang, Yaowei Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Lab Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350301298

dvision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://***/XiangyangLi20/KERM.

关键词： language reasoning vision

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AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

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

作者： Gao, Yipeng Lin, Kun-Yu Yan, Junkai Wang, Yaowei Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pengcheng Lab Shenzhen Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://***/Hlings/AsyFOD.

关键词： detection recognition: Categorization retrieval

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MDL-NAS: A Joint Multi-domain Learning Framework for vision Transformer

MDL-NAS: A Joint Multi-domain Learning Framework for Vision ...

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

作者： Wang, Shiguang Xie, Tao Cheng, Jian Zhang, Xingcheng Liu, Haijun Univ Elect Sci & Technol China Chengdu Peoples R China Harbin Inst Technol Harbin Peoples R China SenseTime Res Hong Kong Peoples R China Chongqing Univ Chongqing Peoples R China

ISBN: (纸本)9798350301298

In this work, we introduce MDL-NAS, a unified framework that integrates multiple vision tasks into a manageable supernet and optimizes these tasks collectively under diverse dataset domains. MDL-NAS is storage-efficient since multiple models with a majority of shared parameters can be deposited into a single one. Technically, MDL-NAS constructs a coarse-to-fine search space, where the coarse search space offers various optimal architectures for different tasks while the fine search space provides fine-grained parameter sharing to tackle the inherent obstacles of multi-domain learning. In the fine search space, we suggest two parameter sharing policies, i.e., sequential sharing policy and mask sharing policy. Compared with previous works, such two sharing policies allow for the partial sharing and non-sharing of parameters at each layer of the network, hence attaining real fine-grained parameter sharing. Finally, we present a joint-subnet search algorithm that finds the optimal architecture and sharing parameters for each task within total resource constraints, challenging the traditional practice that downstream vision tasks are typically equipped with backbone networks designed for image classification. Experimentally, we demonstrate that MDL-NAS families fitted with non-hierarchical or hierarchical transformers deliver competitive performance for all tasks compared with state-of-the-art methods while maintaining efficient storage deployment and computation. We also demonstrate that MDL-NAS allows incremental learning and evades catastrophic forgetting when generalizing to a new task.

关键词： Deep learning architectures and techniques

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Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Complementary Intrinsics from Neural Radiance Fields and CNN...

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

作者： Yang, Siqi Cui, Xuanning Zhu, Yongjie Tang, Jiajun Li, Si Yu, Zhaofei Shi, Boxin 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 Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Comp Sci AI Innovat Ctr Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multi-view stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.

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

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CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution

CiaoSR: Continuous Implicit Attention-in-Attention Network f...

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

作者： Cao, Jiezhang Wang, Qin Xian, Yongqin Li, Yawei Ni, Bingbing Pi, Zhiming Zhang, Kai Zhang, Yulun Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Huawei Inc Shenzhen Peoples R China Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no learnable parameters and it neglects the similarity of the visual features;ii) it has a limited receptive field and cannot ensemble relevant features in a large field which are important in an image. To address these issues, this paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. Furthermore, we embed a scale-aware attention in this implicit attention network to exploit additional non-local information. Extensive experiments on benchmark datasets demonstrate CiaoSR significantly outperforms the existing single image SR methods with the same backbone. In addition, CiaoSR also achieves the state-of-the-art performance on the arbitrary-scale SR task. The effectiveness of the method is also demonstrated on the real-world SR setting. More importantly, CiaoSR can be flexibly integrated into any backbone to improve the SR performance.

关键词： Low-level vision

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Video Test-Time Adaptation for Action recognition

Video Test-Time Adaptation for Action Recognition

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

作者： Lin, Wei Mirza, Muhammad Jehanzeb Kozinski, Mateusz Possegger, Horst Kuchne, Hilde Bischof, Horst Graz Univ Technol Inst Comp Graph & Vis Graz Austria Christian Doppler Lab Semant 3D Comp Vis Graz Austria Christian Doppler Lab Embedded Machine Learning Graz Austria Goethe Univ Frankfurt Frankfurt Germany MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. Code will be available at https://***/wlin-at/ViTTA.

关键词： Video: Action and event understanding

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Improving vision-and-Language Navigation by Generating Future-View Image Semantics

Improving Vision-and-Language Navigation by Generating Futur...

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

作者： Li, Jialu Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA

ISBN: (纸本)9798350301298

vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both Room-to-Room dataset and CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths.

关键词： language reasoning vision

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Annealing-based Label-Transfer Learning for Open World Object Detection

Annealing-based Label-Transfer Learning for Open World Objec...

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

作者： Ma, Yuqing Li, Hainan Zhang, Zhange Guo, Jinyang Zhang, Shanghang Gong, Ruihao Liu, Xianglong Beihang Univ SKLSDE Lab Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Peking Univ Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Data Space Hefei Peoples R China

ISBN: (纸本)9798350301298

Open world object detection (OWOD) has attracted extensive attention due to its practicability in the real world. Previous OWOD works manually designed unknown-discover strategies to select unknown proposals from the background, suffering from uncertainties without appropriate priors. In this paper, we claim the learning of object detection could be seen as an object-level feature-entanglement process, where unknown traits are propagated to the known proposals through convolutional operations and could be distilled to benefit unknown recognition without manual selection. Therefore, we propose a simple yet effective Annealing-based Label-Transfer framework, which sufficiently explores the known proposals to alleviate the uncertainties. Specifically, a Label-Transfer Learning paradigm is introduced to decouple the known and unknown features, while a Sawtooth Annealing Scheduling strategy is further employed to rebuild the decision boundaries of the known and unknown classes, thus promoting both known and unknown recognition. Moreover, previous OWOD works neglected the trade-off of known and unknown performance, and we thus introduce a metric called Equilibrium Index to comprehensively evaluate the effectiveness of the OWOD models. To the best of our knowledge, this is the first OWOD work without manual unknown selection. Extensive experiments conducted on the common-used benchmark validate that our model achieves superior detection performance (200% unknown mAP improvement with the even higher known detection performance) compared to other state-of-the-art methods. Our code is available at https://***/DIG-Beihang/***.

关键词： detection recognition: Categorization retrieval

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Progressive Open Space Expansion for Open-Set Model Attribution

Progressive Open Space Expansion for Open-Set Model Attribut...

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

作者： Yang, Tianyun Wang, Danding Tang, Fan Zhao, Xinying Cao, Juan Tang, Sheng Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhejiang Lab Res Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing openset recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods. Github: https://***/ICTMCG/POSE

关键词： accountability ethics in vision fairness privacy Transparency

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