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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhen Xing Qi Dai Han Hu Jingjing Chen Zuxuan Wu Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia

Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To this end, we introduce SVFormer, which adopts a steady pseudo-labeling framework (i.e., EMA-Teacher) to cope with unlabeled video samples. While a wide range of data augmentations have been shown effective for semi-supervised image classification, they generally produce limited results for video recognition. We therefore introduce a novel augmentation strategy, Tube Token-Mix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis. In addition, we propose a temporal warping augmentation to cover the complex temporal variation in videos, which stretches selected frames to various temporal durations in the clip. Extensive experiments on three datasets Kinetics-400, UCF-101, and HMDB-51 verify the advantage of SVFormer. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400. Our method can hopefully serve as a strong benchmark and encourage future search on semi-supervised action recognition with Transformer networks. Code is released at https://***/ChenHsing/SVFormer.

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Colors display with spatial light modulator based on inf-wavelength metal grating

Colors display with spatial light modulator based on inf-wav...

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2020 Applied Optics and Photonics China: Optoelectronics and Nanophotonics;and Quantum Information Technology, AOPC 2020

作者： Tao, Bo Wang, GuiZhu Wei, Sui Shen, Chuan Xu, ZhangMing Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781510639492

A spatial light modulator based on inf-wavelength gold grating structure, named GLCoS by us, has been devised and tested to meet the challenge of the high space-bandwidth product in holographic video display. A sample GLCoS has been made and its display color appears green. However, if it is to be a device of a display system, its color characteristics must be further investigated, that is, its ability to display RGB color and its color purity. There are two MIM (Metal-Insulator-Metal) in the GLCoS, inf-wavelength gold grating plus liquid crystal cell and aluminum electrode forms a hybridized MIM and inf-wavelength gold grating itself is a MIM acting as an open-end TM-FP (TM-Fabry-Pérot) resonator. Based on our previous study involving GLCoS theoretical model, simulation software platform (FDTD plus CST Studio Suite) and an experimentally verified sample of single color, we seek an engineering way with simplicity and compatibility in the sense of the technique to investigate the red-green-blue (RGB) color display with GLCoS. Among the huge parameter space including the geometry and size of the structure, material, wavelength and light incident angle. We choose to change width of the slit, incident angle, LC (liquid crystal) birefringence to find the appropriate GLCoS spectral characteristics. The simulation results show that the dynamic modulation of the RGB phase can be achieved in the visible light band, and its amount of phase modulation is close to 2π and high chromatographic purity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Light modulators

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Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image

arXiv

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arXiv 2024年

作者： Jiao, Pengkun Zhao, Na Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore

Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pre-trained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios. © 2024, CC BY.

关键词： Object detection

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Exploring Receptance Weighted key Value Model for Single-Channel Speech Enhancement

Exploring Receptance Weighted Key Value Model for Single-Cha...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Yuanle Li Yi Zhou Hongqing Liu School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Signal and Information Processing CQUPT Chongqing China Intelligent Speech and Audio Research Lab (ISARL) CQUPT Chongqing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Speech enhancement has significantly benefited from advancements in deep learning, particularly in terms of intelligibility and perceptual quality. Traditional time-frequency (TF) domain methods rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to predict TF masks or speech spectra. In this study, we introduce a novel network architecture, the Receptance Weighted key Value (RWKV) model integrated with the Deep Complex Convolution Recurrent Network (DC-CRN), aimed at effectively training complex targets. This model combines the strengths of RNNs and Transformers, redefining the attention mechanism for speech enhancement tasks to avoid the exponential growth in computational complexity typically associated with traditional Transformer models. Through a series of experiments on the VoiceBank+Demand dataset, the RWKV-DCCRN model has demonstrated superior performance, efficiency, and scalability in handling long-range dependencies compared to traditional DCCRN models. Our implementation showcases the potential to optimize computational efficiency and performance in real-time speech enhancement applications, making it particularly suitable for environments with varying noise conditions and for devices requiring low latency processing.

关键词： Performance evaluation Training Time-frequency analysis Computational modeling Scalability Refining Speech enhancement Transformers Real-time systems Computational efficiency

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Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning

Masked Video Distillation: Rethinking Masked Feature Modelin...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rui Wang Dongdong Chen Zuxuan Wu Yinpeng Chen Xiyang Dai Mengchen Liu Lu Yuan Yu–Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Cloud + AI

Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel values. In this paper, we propose masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning: firstly we pretrain an image (or video) model by recovering low-level features of masked patches, then we use the resulting features as targets for masked feature modeling. For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks. Visualization analysis also indicates different teachers produce different learned patterns for students. To leverage the advantage of different teachers, we design a spatial-temporal co-teaching method for MVD. Specifically, we distill student models from both video teachers and image teachers by masked feature modeling. Extensive experimental results demonstrate that video transformers pre-trained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets. Our MVD with vanilla ViT achieves state-of-the-art performance compared with previous methods on several challenging video downstream tasks. For example, with the ViT-Large model, our MVD achieves 86.4% and 76.7% Top-1 accuracy on Kinetics-400 and Something-Something-v2, outperforming VideoMAE by 1.2% and 2.4% respectively. When a larger ViT-Huge model is adopted, MVD achieves the state-of-the-art performance with 77.3% Top-1 accuracy on Something-Something-v2. Code will be available at https://***/ruiwang2021/mvd.

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Broadband High Gain Composite VHF Monopole Antenna Design

Broadband High Gain Composite VHF Monopole Antenna Design

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lihua Wang Baofeng Cao Peng Li Zongxiang Li Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China State Key Laboratory of Nuclear and Biochemical Calamity Protection for Civilian Beijing China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A novel wideband miniaturized composite monopole antenna is proposed, analysed and investigated in detail, where the antenna consists of a conical monopole structure with a loaded capacitive disc. The antenna is composed of multiple nested section, aiming to reduce the profile of the antenna. The loading disc at its top is equivalent to a capacitive structure to improve the antenna matching and the stability of the pattern. According to the simulation results, the impedance bandwidth $(\mathbf{S}_{\mathbf{11}} < -\mathbf{10})$ is 132.9% $(\mathbf{60.45}\sim \mathbf{300}\ \mathbf{MHz})$ , or even wider. The gain at the entire frequency averages 3 dBi. The performance of the antenna is very suitable for remote wireless communication.

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An Interactive Navigation Method with Effect-oriented Affordance

An Interactive Navigation Method with Effect-oriented Afford...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaohan Wang Yuehu Liu Xinhang Song Yuyi Liu Sixian Zhang Shuqiang Jiang Institute of Artificial Intelligence and Robotics Xi’ an Jiaotong University Xi'an Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works, visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However, the “messy” environments are more general and practical in our daily lives, where the agent may get blocked by obstacles. Thus Interactive Navigation (InterNav) is introduced to navigate to the objects in more realistic “messy” environments according to the object interaction. Prior work on InterNav learns shortterm interaction through extensive trials with reinforcement learning. However, interaction does not guarantee efficient navigation, that is, plan-ning obstacle interactions that make shorter paths and con-sume less effort is also crucial. In this paper, we introduce an effect-oriented affordance map to enable longterm interactive navigation, extending the existing map-based nav-igation framework to the domain of dynamic environment. We train a set of affordance functions predicting available interactions and the time cost of removing obstacles, which informatively support an interactive modular system to ad-dress interaction and longterm planning. Experiments on the ProcTHOR simulator demonstrate the capability of our affordance-driven system in longterm navigation in complex dynamic environments.

关键词： Visualization Computer vision Uncertainty Costs Navigation Affordances Reinforcement learning

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Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

arXiv

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arXiv 2023年

作者： Xu, Jilan Hou, Junlin Zhang, Yuejie Feng, Rui Wang, Yi Qiao, Yu Xie, Weidi School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Shanghai AI Laboratory China Shanghai Jiaotong University China

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, and aligns the group tokens to the corresponding caption embedding. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, which encourages the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on three benchmark datasets, PASCAL VOC 2012, PASCAL Context, and COCO Object. Our model achieves superior segmentation results over the state-of-the-art method by using only 3% data (4M vs 134M) for pre-training. Code and pre-trained models will be released for future research. © 2023, CC BY.

关键词： Semantics

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Hybrid Near- and Far-Field Source Localization Using Symmetric Sparse Array

Hybrid Near- and Far-Field Source Localization Using Symmetr...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiawen Zhang Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Recently, symmetric linear nonuniform arrays (SLNAs) have been promoted for hybrid near-field (NF) and far-field (FF) source localization. A new SLNA based on symmetric nested array (SNA) is presented in the paper. The proposed array and its difference coarray (DCA) configuration can be expressed in closed-form. Comparing to the existing SLNAs with same sensors number, the proposed array can locate more sources and provide longer consecutive lags, which optimizes the detection results. Simulation results prove that the designed array outperforms traditional compressed symmetric nested array (CSNA).

关键词： Location awareness Simulation Sensors Noise measurement Sensor arrays Antenna arrays

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(Image Present) ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection

arXiv

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arXiv 2024年

作者： Sun, Zhihao Jiang, Haoran Chen, Haoran Cao, Yixin Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science Fudan University China

(Image Present) Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, in this work, we propose ForgerySleuth, which leverages M-LLMs to perform comprehensive clue fusion and generate segmentation outputs indicating specific regions that are tampered with. Moreover, we construct the ForgeryAnalysis dataset through the Chain-of-Clues prompt, which includes analysis and reasoning text to upgrade the image manipulation detection task. A data engine is also introduced to build a larger-scale dataset for the pre-training phase. Our extensive experiments demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in generalization, robustness, and explainability. The resource is available at https://***/sunzhihao18/ForgerySleuth. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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