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Semi-supervised Learning for Few-shot Image-to-Image Translation

Semi-supervised Learning for Few-shot Image-to-Image Transla...

引用

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

作者： Wang, Yaxing Khan, Salman Gonzalez-Garcia, Abel van de Weijer, Joost Khan, Fahad Shahbaz Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden

ISBN: (纸本)9781728171685

In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: https://***/yaxingwang/SEMIT.

关键词： computer vision

来源：评论

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Flexible spatial models for grouping local image features

Flexible spatial models for grouping local image features

引用

Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004

作者： Carneiro, Gustave Jepson, Allan D. Department of Computer Science University of Toronto Toronto Ont. Canada

A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping for long range motion problems.

关键词： Feature extraction

来源：评论

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SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation

SG-Net: Spatial Granularity Network for One-Stage Video Inst...

引用

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

作者： Liu, Dongfang Cui, Yiming Tan, Wenbo Chen, Yingjie Purdue Univ W Lafayette IN 47907 USA Univ Florida Gainesville FL 32611 USA Hangzhou Dian Zi Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization;2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity;3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance;4) Our tracking head models objects' centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a strong and flexible baseline for the VIS task. Our code will be available here(1).

关键词： computer vision Runtime Tracking computer architecture Predictive models Feature extraction Robustness

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Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction

Joint Appearance and Motion Learning for Efficient Rolling S...

引用

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

作者： Fan, Bin Mao, Yuxin Dai, Yuchao Wan, Zhexiong Liu, Qi Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9798350301298

Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inference. In this paper, we propose a single-stage encoder-decoder-based network, named JAMNet, for efficient RSC. It first extracts pyramid features from consecutive RS inputs, and then simultaneously refines the two complementary information (i.e., global shutter appearance and undistortion motion field) to achieve mutual promotion in a joint learning decoder. To inject sufficient motion cues for guiding joint learning, we introduce a transformer-based motion embedding module and propose to pass hidden states across pyramid levels. Moreover, we present a new data augmentation strategy "vertical flip + inverse order" to release the potential of the RSC datasets. Experiments on various benchmarks show that our approach surpasses the state-of-the-art methods by a large margin, especially with a 4.7 dB PSNR leap on real-world RSC. Code is available at https://***/GitCVfb/JAMNet.

关键词： Low-level vision

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NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge

NOC-REK: Novel Object Captioning with Retrieved Vocabulary f...

引用

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

作者： Duc Minh Vo Chen, Hong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing methods heavily rely on an object detection model, we view the detection step as vocabulary retrieval from an external knowledge in the form of embeddings for any object's definition from Wiktionary, where we use in the retrieval image region features learned from a transformers model. We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset. Furthermore, our model eliminates the requirement for model retraining by simply updating the external knowledge whenever a novel object appears. Our comprehensive experiments on held-out COCO and Nocaps datasets show that our NOC-REK is considerably effective against SOTAs.

关键词： Training Vocabulary computer vision Pipelines Training data Object detection Transformers

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

引用

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

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Barath, Daniel Cal Poly Dept Comp Sci & Software Engn San Luis Obispo CA USA Czech Tech Univ Visual Recognit Grp Fac Elect Engn Prague Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Swiss Fed Inst Technol Dept Comp Sci Comp Vision & Geometry Grp Zurich Switzerland

ISBN: (纸本)9798350353006

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https: //***/danini/absolute-pose-from-orientedand-scaled-features.

关键词： absolute pose computer vision geometric vision image-based localization minimal solver

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Domain Prompt Learning with Quaternion Networks

Domain Prompt Learning with Quaternion Networks

引用

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

作者： Cao, Qinglong Xu, Zhengqin Chen, Yuntian Ma, Chao Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Eastern Inst Technol Ningbo Inst Digital Twin Ningbo Peoples R China

ISBN: (纸本)9798350353006

Prompt learning has emerged as a potent and resource-efficient technique in large vision-Language Models (VLMs). However, its application in adapting VLMs to specialized domains like remote sensing and medical imaging, termed domain prompt learning, remains relatively unexplored. Although large-scale domain-specific foundation models offer a potential solution, their focus on a singular vision level presents challenges in prompting both vision and language modalities. To address this limitation, we propose leveraging domain-specific knowledge from these foundation models to transfer the robust recognition abilities of VLMs from generalized to specialized domains, employing quaternion networks. Our method entails utilizing domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within quaternion networks. Furthermore, we introduce a hierarchical approach that derives vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. Through this mechanism, quaternion networks can effectively explore intermodal relationships in specific domains, facilitating domain-specific vision-language contrastive learning. Extensive experiments conducted on domain-specific datasets demonstrate that our proposed method achieves new state-of-the-art results in prompt learning. Codes are available at https://***/caoql98/DPLQ.

关键词： domain prompt learning quaternion networks vision-language models

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Bidirectional Cross-Modal Knowledge Exploration for Video recognition with Pre-trained vision-Language Models

Bidirectional Cross-Modal Knowledge Exploration for Video Re...

引用

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

作者： Wu, Wenhao Wang, Xiaohan Luo, Haipeng Wang, Jingdong Yang, Yi Ouyang, Wanli Univ Sydney Camperdown NSW Australia Baidu Inc Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://***/whwu95/BIKE.

关键词： Video: Action and event understanding

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Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

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Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Unsupervised Learning of Debiased Representations with Pseud...

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

作者： Seo, Seonguk Lee, Joon-Young Han, Bohyung Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAL Seoul South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations. Although existing works often handle this issue based on human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle the limitation, we propose a simple but effective unsupervised debiasing technique. Specifically, we first identify pseudoattributes based on the results from clustering performed in the feature embedding space even without an explicit bias attribute supervision. Then, we employ a novel cluster-wise reweighting scheme to learn debiased representation;the proposed method prevents minority groups from being discounted for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, even achieving the competitive accuracy to the supervised counterpart. The source code is available at our project page(1).

关键词： computer vision Correlation Codes Annotations Computational modeling Machine learning Benchmark testing

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