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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

引用

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

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

引用

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

作者： Liu, Shengjie Zhu, Chuang Xu, Feng Jia, Xinyu Shi, Zhongyue Jin, Mulan Beijing Univ Posts & Telecommun Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://***/BCI.

关键词： Pathology computer vision Image synthesis conferences Breast tissue Epidermis Breast cancer

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

引用

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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DeSI: Deepfake Source Identifier for Social Media

DeSI: Deepfake Source Identifier for Social Media

引用

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

作者： Narayan, Kartik Agarwal, Harsh Mittal, Surbhi Thakral, Kartik Kundu, Suman Vatsa, Mayank Singh, Richa IIT Jodhpur Karwar Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.

关键词： Deepfakes computer vision Social networking (online) Scalability conferences Blogs Sociology

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Unsupervised Continual Learning for Gradually Varying Domains

Unsupervised Continual Learning for Gradually Varying Domain...

引用

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

作者： Taufique, Abu Md Niamul Jahan, Chowdhury Sadman Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In Unsupervised Domain Adaptation (UDA), a network is trained on a source domain and adapted on a target domain where no labeled data is available. Existing UDA techniques consider having the entire target domain available at once, which may not be feasible during deployment in realistic settings where batches of target data are acquired over time. Continual Learning (CL) has been dealing with data constrained paradigms in a supervised manner, where batches of labeled samples are sequentially presented to the network and the network continually learns from the new data without forgetting what was previously learned. Our method for unsupervised continual learning serves as a bridge between the UDA and CL paradigms. This research addresses a gradually evolving target domain fragmented into multiple sequential batches where the model continually adapts to the gradually varying stream of data in an unsupervised manner. To tackle this challenge, we propose a source free method based on episodic memory replay with buffer management. A contrastive loss is incorporated for better alignment of the buffer samples and the continual stream of batches. Our experiments on the rotating MNIST and CORe50 datasets confirm the benefits of our unsupervised continual learning method for gradually varying domains.

关键词： Learning systems Bridges computer vision Adaptation models Codes conferences Memory management

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Contrastive Regularization for Semi-Supervised Learning

Contrastive Regularization for Semi-Supervised Learning

引用

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

作者： Lee, Doyup Kim, Sungwoong Kim, Ildoo Cheon, Yeongjae Cho, Minsu Han, Wook-Shin POSTECH Pohang South Korea Kakao Brain Seongnam South Korea Kakao Enterprise Seongnam South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudolabels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-of-the-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.

关键词： Training computer vision Analytical models conferences Memory management Semisupervised learning Benchmark testing

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Towards Open-Set Object Detection and Discovery

Towards Open-Set Object Detection and Discovery

引用

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

作者： Zheng, Jiyang Li, Weihao Hong, Jie Petersson, Lars Barnes, Nick Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.

关键词： Visualization computer vision Protocols conferences Object detection Detectors Human in the loop

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Transformer-based Multimodal Information Fusion for Facial Expression Analysis

Transformer-based Multimodal Information Fusion for Facial E...

引用

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

作者： Zhang, Wei Qiu, Feng Wang, Suzhen Zeng, Hao Zhang, Zhimeng An, Rudong Ma, Bowen Ding, Yu Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human affective behavior analysis has received much attention in human-computer interaction (HCI). In this paper, we introduce our submission to the CVPR 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To fully exploit affective knowledge from multiple views, we utilize the multimodal features of spoken words, speech prosody, and facial expression, which are extracted from the video clips in the Aff-Wild2 dataset. Based on these features, we propose a unified transformer-based multimodal framework for Action Unit detection and also expression recognition. Specifically, the static vision feature is first encoded from the current frame image. At the same time, we clip its adjacent frames by a sliding window and extract three kinds of multimodal features from the sequence of images, audio, and text. Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features. The cross-attention module in the fusion module makes the output integrated features focus on the crucial parts that facilitate the downstream detection tasks. We also leverage some data balancing techniques, data augmentation techniques, and postprocessing methods to further improve the model performance. In the official test of ABAW3 Competition, our model ranks first in the EXPR and AU tracks. The extensive quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset, prove the effectiveness of our proposed method.

关键词： Human computer interaction Gold Face recognition conferences Feature extraction Transformers Data models

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ECO: Ensembling Context Optimization for vision-Language Models

ECO: Ensembling Context Optimization for Vision-Language Mod...

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ieee/CVF International conference on computer vision (ICCV)

作者： Agnolucci, Lorenzo Baldrati, Alberto Todino, Francesco Becattini, Federico Bertini, Marco Del Bimbo, Alberto Univ Florence Florence Italy Univ Siena Siena Italy Univ Pisa Pisa Italy

ISBN: (纸本)9798350307443

Image recognition has recently witnessed a paradigm shift, where vision-language models are now used to perform few-shot classification based on textual prompts. Among these, the CLIP model has shown remarkable capabilities for zero-shot transfer by matching an image and a custom textual prompt in its latent space. This has paved the way for several works that focus on engineering or learning textual contexts for maximizing CLIP's classification capabilities. In this paper, we follow this trend by learning an ensemble of prompts for image classification. We show that learning diverse and possibly shorter contexts improves considerably and consistently the results rather than relying on a single trainable prompt. In particular, we report better few-shot capabilities with no additional cost at inference time. We demonstrate the capabilities of our approach on 11 different benchmarks.

关键词： CLIP CoOP Few shot classification Few shot learning ImageNet Prompt ensembling Prompt learning vision and Language

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Towards Real-world Shadow Removal with a Shadow Simulation Method and a Two-stage Framework

Towards Real-world Shadow Removal with a Shadow Simulation M...

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

作者： Gao, Jianhao Zheng, Quanlong Guo, Yandong Wuhan Univ Wuhan Peoples R China OPPO Res Inst Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://***/jianhaogao/Shadow-Removal-with-Two-stage-Framework.

关键词： Training computer vision Image color analysis conferences Computational modeling Gray-scale Robustness

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