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Multimodal Prompting with Missing Modalities for Visual recognition

Multimodal Prompting with Missing Modalities for Visual Reco...

引用

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

作者： Lee, Yi-Lun Tsai, Yi-Hsuan Chiu, Wei-Chen Lee, Chen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Google Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations;and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model re-training. Code is available.

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Tracking Skiers from the Top to the Bottom

Tracking Skiers from the Top to the Bottom

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Dunnhofer, Maueo Sordi, Luca Martine', Niki Micheloni, Christian Univ Udine Machine Learning & Percept Lab Udine Italy

ISBN: (纸本)9798350318920;9798350318937

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://***/datasets/skitb.

关键词： Algorithms Applications Social good Video recognition and understanding

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Revisiting the Stack-Based Inverse Tone Mapping

Revisiting the Stack-Based Inverse Tone Mapping

引用

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

作者： Zhang, Ning Ye, Yuyao Zhao, Yang Wang, Ronggang Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up and three exposure-down), which may introduce unnecessary computational cost or reconstruct incorrect results. On the other hand, they neglect the connections between the upexposure and down-exposure models and thus fail to fully excavate effective features. In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images. At first, we design the exposure adaptive block that can adaptively adjust the exposure based on the luminance distribution of the input image. Secondly, we devise the cross-model attention block to connect the exposure adjustment models. Thirdly, we propose an end-to-end ITM pipeline by incorporating the multi-exposure fusion model. Furthermore, we propose and open a multi-exposure dataset that indicates the optimal exposure-up/down levels. Experimental results show that the proposed method outperforms some state-of-the-art methods.

关键词： Low-level vision

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All-in-one Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations

All-in-one Image Restoration for Unknown Degradations Using ...

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

作者： Park, Dongwon Lee, Byung Hyun Chun, Se Young Seoul Natl Univ INMC Seoul South Korea Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea UNIST Dept EE Ulsan South Korea

ISBN: (纸本)9798350301298

Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time. To mitigate this issue, image restorations for known and unknown multiple degradations have recently been investigated, showing promising results, but require large networks or have sub-optimal architectures for potential interference among different degradations. Here, inspired by the filter attribution integrated gradients (FAIG), we propose an adaptive discriminative filter-based model for specific degradations (ADMS) to restore images with unknown degradations. Our method allows the network to contain degradation-dedicated filters only for about 3% of all network parameters per each degradation and to apply them adaptively via degradation classification (DC) to explicitly disentangle the network for multiple degradations. Our proposed method has demonstrated its effectiveness in comparison studies and achieved state-of-the-art performance in all-in-one image restoration benchmark datasets of both Rain-Noise-Blur and Rain-Snow-Haze.

关键词： Low-level vision

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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

作者： Zhang, Xi Zhang, Feifei Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China

ISBN: (纸本)9798350301298

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

关键词： language reasoning vision

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Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval

Cross-Modal Implicit Relation Reasoning and Aligning for Tex...

引用

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

作者： Jiang, Ding Ye, Mang Wuhan Univ Inst Artificial Intelligence Hubei Key Lab Multimedia & Network Commun Natl Engn Res Ctr Multimedia SoftwareSch Comp Sc Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (纸本)9798350301298

Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods.

关键词： and reasoning language vision

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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

Bidirectional Copy-Paste for Semi-Supervised Medical Image S...

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

作者： Bai, Yunhao Chen, Duowen Li, Qingli Shen, Wei Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is avaiable at https://***/DeepMed-Lab-ECNU/BCP.

关键词： cell microscopy Medical and biological vision

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EC² : Emergent Communication for Embodied Control

EC<SUP>2</SUP> : Emergent Communication for Embodied Control

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

作者： Mu, Yao Yao, Shunyu Ding, Mingyu Luo, Ping Gan, Chuang Univ Hong Kong Hong Kong Peoples R China Princeton Univ Princeton NJ USA UMass Amherst Amherst MA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Embodied control requires agents to leverage multimodal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.

关键词： Embodied vision: Active agents simulation

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Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving

Visual Exemplar Driven Task-Prompting for Unified Perception...

引用

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

作者： Liang, Xiwen Niu, Minzhe Han, Jianhua Xu, Hang Xu, Chunjing Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, these algorithms are designed for different tasks mostly not within the scope of autonomous driving, thus making it hard to compare multi-task methods in autonomous driving. Aiming to enable the comprehensive evaluation of present multi-task learning methods in autonomous driving, we extensively investigate the performance of popular multi-task methods on the large-scale driving dataset, which covers four common perception tasks, i.e., object detection, semantic segmentation, drivable area segmentation, and lane detection. We provide an in-depth analysis of current multi-task learning methods under different common settings and find out that the existing methods make progress but there is still a large performance gap compared with single-task baselines. To alleviate this dilemma in autonomous driving, we present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting to guide the model toward learning high-quality task-specific representations. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories and further mitigate the performance gap. Furthermore, we bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving. Comprehensive experimental results on the diverse self-driving dataset BDD100K show that the VE-Prompt improves the multi-task baseline and further surpasses single-task models.

关键词： vision applications and systems

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Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation

Dynamic Graph Enhanced Contrastive Learning for Chest X-ray ...

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

作者： Li, Mingjie Lin, Bingqian Chen, Zicong Lin, Haokun Liang, Xiaodan Chang, Xiaojun Univ Technol Sydney ReLER AAII Sydney Australia Sun Yat Sen Univ Sch ISE Guangzhou Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Comp Vis Abu Dhabi U Arab Emirates Peng Cheng Natl Lab Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.

关键词： cell microscopy Medical and biological vision

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