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检索条件"机构=MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition"

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Beyond Pixel and Object: Part Feature as Reference for Few-Shot Video Object Segmentation 39

Beyond Pixel and Object: Part Feature as Reference for Few-S...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Naisong Xiong, Guoxin Zhang, Tianzhu MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

ISBN: (纸本)157735897X

Few-Shot Video Object Segmentation (FSVOS) aims to achieve accurate segmentation of video sequences supported by limited annotated images. In this work, we analyze the deficiencies inherent in the use of object prototypes and pixel features as references in previous methods. Then we shed light on that part features, with the ability to adapt to appearance variations and resist noise, are advantageous as representative reference features for aligning support images and query videos. Therefore, we propose a Part Agent Learning Network (PALN) to leverage part features from two aspects. First, we elaborately employ Optimal Transport algorithm with equal partition constraint to make part agents capable of dividing support objects into diverse parts in an adaptive manner. Second, we design a dedicated cache mechanism to learn temporal part agents as lightweight historic target representation to exploit temporal consistency. With the aid of these learned part agents, our PALN can effectively achieve support-query alignment and temporal alignment for accurate segmentation of query videos. Extensive experimental results on two challenging benchmarks demonstrate that our method performs favorably against state-of-the-art FSVOS methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Learning Complementary Maps for Light Field Salient Object Detection 17th

Learning Complementary Maps for Light Field Salient Object ...

引用

17th Asian Conference on Computer Vision, ACCV 2024

作者： Xiao, Zeyu Shou, Jiateng Xiong, Zhiwei MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9789819609161

Light field imaging presents a promising avenue for advancing salient object detection (SOD). However, existing light field SOD (LFSOD) methods grapple with challenges related to effectively aggregating features from all-in-focus (AiF) images and focal slices. These methods often under-utilize the complementary nature of salient and non-saliency maps, leading to inaccurate predictions, particularly at fine boundaries. To tackle these limitations, in this paper, we introduce a novel method for LFSOD. Our method incorporates a Cross-Modality Aggregation (CMA) module at multiple levels, facilitating the efficient fusion of AiF image and focal slice features. This progressive aggregation capitalizes on global and local dependencies to harness implicit geometric information in an LF. Based on the observation that, salient regions and non-salient counterparts are complementary to each other, thus a better estimation on one side leads to an improved estimation on the other, and vice versa, we introduce the Complementary Saliency Map Generator (CSMG). The CSMG generates both saliency and non-saliency maps interactively to leverage the inherent complementary relationship between salient regions and their non-salient counterparts. Through extensive experiments conducted on benchmark datasets, we have demonstrated that our proposed method achieves superior performance in LFSOD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Complementary map Light field Salient object detection

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DiffLoss: Unleashing Diffusion Model as Constraint for Training Image Restoration Network 17th

DiffLoss: Unleashing Diffusion Model as Constraint for Tra...

引用

17th Asian Conference on Computer Vision, ACCV 2024

作者： Tan, Jiangtong Yu, Hu Huang, Jie Yang, Zizheng Zhao, Feng MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9789819609109

Image restoration aims to enhance low-quality images, producing high-quality images that exhibit natural visual characteristics and fine semantic attributes. Recently, the diffusion model has emerged as a powerful technique for image generation, and it has been explicitly employed as a backbone in image restoration tasks, yielding excellent results. However, it suffers from the drawbacks of slow inference speed and large model parameters due to its intrinsic characteristics. In this paper, we introduce a new perspective that implicitly leverages the diffusion model to assist the training of image restoration network, called DiffLoss, which drives the restoration results to be optimized for naturalness and semantic-aware visual effect. To achieve this, we utilize the mode coverage capability of the diffusion model to approximate the distribution of natural images and explore its ability to capture image semantic attributes. On the one hand, we extract intermediate noise to leverage its modeling capability of the distribution of natural images, which serves as a naturalness-oriented optimization space. On the other hand, we utilize the bottleneck features of diffusion model to harness its semantic attributes serving as a constraint on semantic level. By combining these two designs, the overall loss function is able to improve the perceptual quality of image restoration, resulting in visually pleasing and semantically enhanced outcomes. To validate the effectiveness of our method, we conduct experiments on various common image restoration tasks and benchmarks. Extensive experimental results demonstrate that our approach enhances the visual quality and semantic perception of the restoration network. Code is available at https://***/JosephTiTan/DiffLoss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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Neural Krylov Iteration for Accelerating Linear System Solving 38

Neural Krylov Iteration for Accelerating Linear System Solvi...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Luo, Jian Wang, Jie Wang, Hong Dong, Huanshuo Geng, Zijie Chen, Hanzhu Kuang, Yufei MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

Solving large-scale sparse linear systems is essential in fields like mathematics, science, and engineering. Traditional numerical solvers, mainly based on the Krylov subspace iteration algorithm, suffer from the low-efficiency problem, which primarily arises from the less-than-ideal iteration. To tackle this problem, we propose a novel method, namely Neural Krylov Iteration (NeurKItt), for accelerating linear system solving. Specifically, NeurKItt employs a neural operator to predict the invariant subspace of the linear system and then leverages the predicted subspace to accelerate linear system solving. To enhance the subspace prediction accuracy, we utilize QR decomposition for the neural operator outputs and introduce a novel projection loss function for training. NeurKItt benefits the solving by using the predicted subspace to guide the iteration process, significantly reducing the number of iterations. We provide extensive experiments and comprehensive theoretical analyses to demonstrate the feasibility and efficiency of NeurKItt. In our main experiments, NeurKItt accelerates the solving of linear systems across various settings and datasets, achieving up to a 5.5× speedup in computation time and a 16.1× speedup in the number of iterations. © 2024 Neural information processing systems foundation. All rights reserved.

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Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions 38

Uncertainty-based Offline Variational Bayesian Reinforcement...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Rui Wang, Jie Wu, Guoping Li, Bin University of Science and Technology of China China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition China

Real-world offline datasets are often subject to data corruptions (such as noise or adversarial attacks) due to sensor failures or malicious attacks. Despite advances in robust offline reinforcement learning (RL), existing methods struggle to learn robust agents under high uncertainty caused by the diverse corrupted data (i.e., corrupted states, actions, rewards, and dynamics), leading to performance degradation in clean environments. To tackle this problem, we propose a novel robust variational Bayesian inference for offline RL (TRACER). It introduces Bayesian inference for the first time to capture the uncertainty via offline data for robustness against all types of data corruptions. Specifically, TRACER first models all corruptions as the uncertainty in the action-value function. Then, to capture such uncertainty, it uses all offline data as the observations to approximate the posterior distribution of the action-value function under a Bayesian inference framework. An appealing feature of TRACER is that it can distinguish corrupted data from clean data using an entropy-based uncertainty measure, since corrupted data often induces higher uncertainty and entropy. Based on the aforementioned measure, TRACER can regulate the loss associated with corrupted data to reduce its influence, thereby enhancing robustness and performance in clean environments. Experiments demonstrate that TRACER significantly outperforms several state-of-the-art approaches across both individual and simultaneous data corruptions. © 2024 Neural information processing systems foundation. All rights reserved.

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Hierarchical Augmentation Consistency Learning for Semi-Supervised Medical Image Segmentation 22

Hierarchical Augmentation Consistency Learning for Semi-Supe...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Xiao, Yusong Xiao, Li University of Science and Technology of China MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition Hefei230052 China

ISBN: (纸本)9798331520526

While convolutional neural network (CNN) based methods have driven pivotal advancements in medical image segmentation, they remain constrained by the heavy reliance on large-scale labeled datasets. Semi-supervised learning(SSL) has recently emerged as a promising alternative to mitigate this limitation by effectively leveraging unlabeled data. In this paper, we propose a novel semi-supervised medical image segmentation method based on a teacher-student network. We perform strong and weak data augmentations on the original unlabeled data to broaden the data distribution while ensuring predictive consistency between augmented variants. Specifically, the teacher model generates stable pseudo-labels in weakly augmented images, while the student model learns how to generate corresponding predictions in strongly augmented data. Moreover, the weakly augmented images and labeled images are generated as mixed images by a copy-paste strategy, while the predictions of the mixed images on the student model are constrained for consistency regularization by the associated outputs in the same copy-paste way. We conduct extensive experiments on the 'Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation' dataset for the segmentation of anterior lip and posterior lip, and the results show that our method outperforms currently advanced semi-supervised image segmentation methods. © 2025 IEEE.

关键词： Image segmentation

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Training Pansharpening Networks at Full Resolution Using Degenerate Invariance 24

Training Pansharpening Networks at Full Resolution Using Deg...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Qu, Yichang Li, Bing Huang, Jie Zhao, Feng MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9798400706868

Pansharpening is an important technique for remote sensing imaging systems to obtain high-resolution multispectral images. Existing deep learning-based methods mostly rely on using pseudo-groundtruth multi-spectral images for supervised learning. The whole training process only remains at the scale of reduced resolution, which means that the impact of the degradation process is ignored and high-quality images cannot be guaranteed at full resolution. To address the challenge, we propose a new unsupervised framework that does not rely on pseudo-groundtruth but uses the invariance of the degradation process to build a consistent loss function on the original scale for network training. Specifically, we first introduce the operator learning method to build an exact mapping function from multi-spectral to panchromatic images and decouple both spectral and texture features. Then, through joint training, operators and convolutional networks can learn the spatial degradation process and spectral degradation process at full resolution, respectively. By introducing them to build consistency constraints, we can train the pansharpening network at the original full resolution. Our approach can be applied to existing pansharpening methods, improving their usability on original data, which matches practical application requirements. The experimental results on different kinds of satellite datasets demonstrate that the proposed network outperforms state-of-the-art methods both visually and quantitatively. Our code is available at https://***/quycruin/Qvac. © 2024 ACM.

关键词： Deep learning

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FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining 24

FreqMamba: Viewing Mamba from a Frequency Perspective for Im...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zou, Zhen Yu, Hu Huang, Jie Zhao, Feng MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9798400706868

Images corrupted by rain streaks often lose vital frequency information for perception, and image deraining aims to solve this problem, which relies on global and local degradation modeling. Recent studies have witnessed the effectiveness and efficiency of Mamba for perceiving global and local information based on its exploiting local correlation among patches, however, rarely attempts have been explored to extend it with frequency analysis for image deraining, limiting its ability to perceive global degradation that is relevant to frequency modeling (e.g. Fourier transform). In this paper, we propose FreqMamba, an effective and efficient paradigm that leverages the complementary between Mamba and frequency analysis for image deraining. The core of our method lies in extending Mamba with frequency analysis from two perspectives: extending it with frequency band for exploiting frequency correlation, and connecting it with Fourier transform for global degradation modeling. Specifically, FreqMamba introduces complementary triple interaction structures including spatial Mamba, frequency-band Mamba, and Fourier global modeling. Frequency-Band Mamba decomposes the image into sub-bands of different frequencies to allow 2D scanning from the frequency dimension. Furthermore, leveraging Mamba's unique data-dependent properties, we use rainy images at different scales to provide degradation priors to the network, thereby facilitating efficient training. Extensive experiments show that our method outperforms state-of-the-art methods both visually and quantitatively. Our code is available at: https://***/aSleepyTree/FreqMamba. © 2024 ACM.

关键词： Fourier transforms

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Semantic Segmentation-Based Low-Rate Image Communication With Diffusion Models 16

Semantic Segmentation-Based Low-Rate Image Communication Wit...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Huang, Jun Liu, Chang Liu, Dong Moe Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9798350390643

Semantic communication aims to transmit the underlying semantic information of a signal from the sender to the receiver, where the key requirement is to ensure that the receiver reconstructs a signal semantically (almost) equivalent to the source. Conventional image communication methods lack effective mechanisms for preserving both semantic coherence and reconstruction quality, especially for low-rate scenarios. In this paper, we propose an alternative method, namely Instance-Consistent Image Communication (ICIC), for semantic segmentation-based low-rate image communication. Our method leverages segmented instances and detected captions as intermediate vehicles to transmit semantic information from the sender to the receiver. Once transmitted, the receiver is capable of utilizing the vehicles to regenerate images with off-the-shelf diffusion models. Compared to state-of-the-art methods, experimental results on the COCO dataset illustrate that our method obtains higher semantic accuracy and higher image quality at extremely low rates. © 2024 IEEE.

关键词： Semantic Segmentation

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Hierarchical Supervised Contrastive Learning for Multimodal Sentiment Analysis 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Chen, Kezhou Wang, Shuo Hao, Yanbin MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Multimodal sentiment analysis (MSA) is dedicated to deciphering human emotions in videos. It is a challenging task due to the semantic disparities among various modalities (e.g., linguistic, visual, and acoustic) present in video content. To bridge these gaps, we leverage contrastive learning and introduce a novel hierarchical multimodal approach termed Hierarchical Supervised Contrastive Learning (HSCL). Initially, we utilize an unimodal fusion combined with a supervised contrastive learning strategy to distill pertinent content from each modality. Subsequently, we combine the bimodal data in pairs and further align them through supervised contrastive learning. This paired data aids in understanding the intricate nuances of multidimensional human emotions. In our method, supervised contrastive learning is tailored to accentuate the significance of label information, facilitating the extraction of sentiment cues from diverse sources. Experimental studies on two benchmark datasets demonstrate the effectiveness of our method. Specifically, compared to the baseline on the CMU-MOSI benchmark, our method achieves 2.59% accuracy improvement for emotion recognition. Codes are released at https://***/Turdidae810/HSCL. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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