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Decoupled Feature Matching for Few-shot Counting and Localization

Decoupled Feature Matching for Few-shot Counting and Localiz...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chao Zhai Fan Zhang Wenyu Chen Malu Zhang Fan Li Xuanting Xie University of Electronic Science and Technology of China Chengdu China The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Few-shot counting (FSC) aims to train a generalized visual counting model that can count any novel category given a small number of support samples. Current prevalent approaches treat FSC as a feature-matching task, leveraging attention to aggregate information from all other query patches or supports for each query patch. However, we notice that this operation blends target features with non-target features, making it difficult for the model to differentiate between targets and non-targets, thereby impacting counting accuracy. To tackle this issue, we develop a Decoupled Feature Matching Module (DFMM), which decouples target and non-target regions and conducts self-aggregation within respective regions. Furthermore, we design a Consistency Alignment Loss (CAL) to facilitate discriminative ability between targets and non-targets across multiple scales. Besides, we adopt a localization paradigm for counting and propose an Anchor-based Assignment Strategy to stabilize the optimization process and improve counting accuracy. Experiments on FSC147 and CARPK demonstrate that our method can achieve performance on par with state-of-the-art methods. Qualitative and quantitative experiments both confirm the efficacy of our proposed components.

关键词： Location awareness Training Visualization Accuracy Aggregates signal processing Acoustics Power capacitors Speech processing Optimization

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Multi-view Feature Discrepancy Attack for Single Object Tracking

Multi-view Feature Discrepancy Attack for Single Object Trac...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhiheng Li Weng Zhimin Yuehuan Wang Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adversarial attacks on single object tracking (SOT) have attracted increasing attention. However, most previous works have focused on adding small digital perturbations to tracking sequences, assuming access to the data, which makes these attacks impractical in real-world applications. Inspired by traditional military camouflage, we propose a texture pattern attack method with a similar implementation, called the Multi-View Feature Discrepancy Attack (MFDA). Unlike the consistent texture features of traditional camouflage, we iteratively optimize non-planar textures by obtaining gradients from the target model to enhance feature discrepancies across different viewpoints. Moreover, the model’s multi-scale features and heatmaps are utilized as targets for our feature-level and decision-level attacks, respectively. We apply texture patterns to controllable regions of vehicle models and conduct extensive attack experiments. The results show that our method significantly degrades the performance of state-of-the-art Siamese-based trackers, and also exhibits attack capability against ViT-based trackers.

关键词： Heating systems Target tracking Perturbation methods signal processing Acoustics Surface texture Object tracking Speech processing Autonomous vehicles

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Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qiankun Pi Jicang Lu Yepeng Sun Qinlong Fan Xukun Zhou Shouxin Shang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model’s understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model’s adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model’s stance discriminative ability and generalization performance.

关键词： Adaptation models Weak supervision Social networking (online) Annotations Semantics Blogs Transfer learning signal processing data models Speech processing

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Performance of Range Doppler Algorithm in Synthetic Aperture Radar Image Formation for Semi-urban Terrain

Performance of Range Doppler Algorithm in Synthetic Aperture...

引用

International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Sai Meghana M Venkata Sai Chandana K Venkateswara Reddy M Sanjika Devi R. V. Dhanesh G. Kurup Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

This paper presents the application of range Doppler algorithm (RDA) on Synthetic Aperture Radar (SAR) image formation for semi-urban terrain. The proposed approach offers significant insights into the capabilities of RDA for accurate terrain mapping and target detection by effectively simulating real-world scenarios that involve the acquisition of raw SAR data from discrete point sources with varying reflectivity. The proposed work involves emulating a SAR system by positioning it at a specific altitude to detect ground targets spread across an area of 100m 2 . The range Doppler algorithm effectively processes data from high altitudes and precisely distinguishes between targets, yielding exact SAR images. In addition, RDA achieves computational efficiency by leveraging FFT-based techniques, reducing processing complexity in both range compression and azimuth filtering, and using range-dependent phase corrections for secondary range compression (SRC) and range cell migration correction (RCMC) techniques.

关键词： Wireless communication Reflectivity Image coding signal processing algorithms Terrain mapping Object detection signal processing Radar polarimetry Doppler effect Synthetic aperture radar

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Deep Learning Amplified Early Stopping Bias: Overestimating Performance on small datasets

Deep Learning Amplified Early Stopping Bias: Overestimating ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Nona Rajabi Antônio H. Ribeiro Miguel Vasco Danica Kragic Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Information Technology Uppsala University Uppsala Sweden

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-validation is commonly used to estimate machine learning model performance on new samples. However, using it for both hyperparameter selection and error estimation can lead to overestimating model performance, especially with extensive hyperparameter searches that overly tailor models to validation data. We demonstrate that deep learning further amplifies this bias, with even minor model adjustments causing significant overestimation. Our extensive experiments on simulated and real data focus on the bias from early stopping during cross-validation. We find that overestimation intensifies with network depth and is especially severe in small datasets, which are common in physiological signal processing applications. Selecting the early stopping point during cross-validation can result in ROC-AUC estimates exceeding 90% on random data, and this effect persists across various sample sizes, architectures, and network sizes 1 .

关键词： Deep learning Error analysis signal processing data models Physiology Acoustics Speech processing

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Experimental Investigation for GPR Detection of Buried Objects with Varied Sizes and Orientation

Experimental Investigation for GPR Detection of Buried Objec...

引用

Power, Control and Computing Technologies (ICPC2T), conference on

作者： Sujeet Roy Himanshu Govil Sandip Mukherjee Prabhat Diwan Department of Applied Geology National Institute of Technology Raipur India Independent Researcher Indian Institute of Remote Sensing Dehradun India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Ground Penetration Radar (GPR) is a remote sensing technology with immense potential. Its performance and quality of results strongly depends on primarily the composition of the soil and further on other aspects of the subsurface targets like the size, material, shape, depth, and orientation. The central operating frequency of the GPR and the type of antenna on the GPR are selected based on the factors above, to be able to detect your intended target satisfactorily. Limited exploratory research work has been undertaken in this field in India towards detection of small shallow buried objects. The present study attempts to experimentally investigate the combined effect of predetermined size and orientation of small shallow buried objects in conjunction with a suitably selected central frequency of the GPR. The experiment was conducted in Jaisalmer, where the arid dry sandy wastelands are conducive to GPR performance. signal processing methods have been applied to the standard GPR results to derive better output. Apart from the standard effect of material of the targets and the depth buried to, the study mainly provided data indicating a positive correlation between the central operating frequency of the GPR and the quality of detection of the shallow subsurface targets of size corresponding to the wavelength $\lambda$. The positive correlation of polarization of the GPR antenna to the alignment of the buried object was also noted. Further investigations covering more factors affecting GPR performance is advocated by the present study.

关键词： Surveys Correlation Soil Planning data mining Test equipment Buried object detection Standards Remote sensing Antennas

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Lunar Tracking: A New Benchmark For Nighttime Tiny Object Tracking

Lunar Tracking: A New Benchmark For Nighttime Tiny Object Tr...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mohammed Leo Ding Zhang Hai-Tao Zheng Haiye Lin Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Many existing visual object tracking benchmarks, such as TNL2k, TrackingNet, LaSOT, and GOT-10K, primarily focus on daytime scenarios. However, the challenge of tracking small targets in low-light conditions has not been sufficiently addressed. This limitation is due to the absence of a large-scale, meticulously annotated nighttime benchmark that could rigorously evaluate tracking algorithms designed for tiny targets. To bridge this gap, we introduce Lunar Tracking, a groundbreaking benchmark tailored to assess the performance of visual object tracking algorithms on tiny objects under low-light conditions. The Lunar Tracking benchmark comprises 700 diverse videos with over 600,000 annotated frames featuring tiny targets, making it the largest of its kind for nighttime tracking and tiny object tracking. We also introduce a novel tracking algorithm designed to enhance low-light conditions by innovatively treating the light enhancement task as a ’dehazing’ process and modeling it as a two-step diffusion process. Our proposed module has set a new state-of-the-art (SOTA) across the Lunar Tracking benchmark and four additional benchmarks by reversing the diffusion process in a step-by-step manner. Our project can be found at https://***/kk123321x/LunarTracking.

关键词： Training Visualization Target tracking Moon signal processing algorithms Diffusion processes Benchmark testing Object tracking Speech processing Videos

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Zero-shot Stance Detection with Logically Consistent data Augmentation

Zero-shot Stance Detection with Logically Consistent Data Au...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bowen Zhang Xu Li Jun Ma Xi Zhang Genan Dai Jianhua Ye College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Science Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets. Existing data augmentation methods for ZSSD often suffer from semantic inconsistencies, hindering their effectiveness. To address these limitations, we propose a Logically Consistent data Augmentation (LCDA) framework that enhances data quality by maintaining logical coherence. LCDA comprises two key components: data augmentation and logical graph convolutional network. Our data augmentation approach leverages chain-of-thought (CoT) prompting to enable large language models (LLMs) to distill training text into first-order logic (FOL) expressions, which provide a clear and concise representation of the underlying logic for stance prediction. These FOL expressions are then used to generate high-quality augmented samples. Furthermore, we introduce a novel logical graph convolutional network that effectively exploits FOL knowledge and combines it with augmented data samples to train a more accurate stance detection model. Our experiments on benchmark datasets demonstrate that the LCDA framework significantly outperforms existing ZSSD techniques, highlighting the effectiveness of integrating FOL into data augmentation for improving stance detection accuracy.

关键词： Training Accuracy Graph convolutional networks data integrity Semantics Coherence signal processing data augmentation Logic Speech processing

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Integrated Long-range Sensing and Communications in Multi Target Scenarios using CP-OFDM

Integrated Long-range Sensing and Communications in Multi Ta...

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International ITG conference on Source and Channel Coding (SCC)

作者： Benedikt Geiger Silvio Mandelli Marcus Henninger Daniel Gil Gaviria Charlotte Muth Laurent Schmalen Communications Engineering Lab (CEL) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Nokia Bell Labs Stuttgart Stuttgart Germany

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

6G communication systems promise to deliver sensing capabilities by utilizing the orthogonal frequency division multiplexing (OFDM) communication signal for sensing. How-ever, the cyclic prefix inherent in OFDM systems limits the sensing range, necessitating compensation techniques to detect small, distant targets like drones. In this paper, we show that state-of-the-art coherent compensation methods fail in scenarios involving multiple targets, resulting in an increased noise floor in the radar image. Our contributions include a novel multi target coherent compensation algorithm and a generalized signal-to-interference-and-noise ratio for multiple targets to evaluate the performance. Our algorithm achieves the same detection performance at long distances requiring only 3.6 % of the radio resources compared to classical OFDM radar processing. This enables resource efficient sensing at long distances in multi target scenarios with legacy communications-only networks.

关键词： 6G mobile communication OFDM Radar detection Interference Radar Radar imaging Sensors Reliability signal to noise ratio Drones

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Biodenoising: Animal Vocalization Denoising without Access to Clean data

Biodenoising: Animal Vocalization Denoising without Access t...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Marius Miron Sara Keen Jen-Yu Liu Benjamin Hoffman Masato Hagiwara Olivier Pietquin Felix Effenberger Maddie Cusimano Earth Species Project

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Animal vocalization denoising is a task similar to human speech enhancement, which is relatively well-studied. In contrast to the latter, it comprises a higher diversity of sound production mechanisms and recording environments, and this higher diversity is a challenge for existing models. Adding to the challenge and in contrast to speech, we lack large and diverse datasets comprising clean vocalizations. As a solution we use as training data pseudo-clean targets, i.e. pre-denoised vocalizations, and segments of background noise without a vocalization. We propose a train set derived from bioacoustics datasets and repositories representing diverse species, acoustic environments, geographic regions. Additionally, we introduce a non-overlapping benchmark set comprising clean vocalizations from different taxa and noise samples. We show that that denoising models (demucs, CleanUNet) trained on pseudo-clean targets obtained with speech enhancement models achieve competitive results on the benchmarking set. We publish data, code, libraries, and demos at https://***/research/biodenoising.

关键词： Deep learning Adaptation models Animals Biological system modeling Noise reduction Training data Speech enhancement Benchmark testing Recording Biomedical acoustics

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