arXiv subjects

Dingding Yao

Publications and source records attributed to Dingding Yao.

7 recordsLinked to original sources

arXiv2026

Think and Answer ME: Benchmarking and Exploring Multi-Entity Reasoning Grounding in Remote Sensing

Recent advances in reasoning language models and reinforcement learning with verifiable rewards have significantly enhanced multi-step reasoning capabilities. This progress motivates the extension of reasoning paradigms to remote sensing visual grounding task. However, existing remote sensing grounding methods remain largely confined to perception-level matching and single-entity formulations, limiting the role of explicit reasoning and inter-entity modeling. To address this challenge, we introduce a new benchmark dataset for Multi-Entity Reasoning Grounding in Remote Sensing (ME-RSRG). Based on ME-RSRG, we reformulate remote sensing grounding as a multi-entity reasoning task and propose an Entity-Aware Reasoning (EAR) framework built upon visual-linguistic foundation models. EAR generates structured reasoning traces and subject-object grounding outputs. It adopts supervised fine-tuning for cold-start initialization and is further optimized via entity-aware reward-driven Group Relative Policy Optimization (GRPO). Extensive experiments on ME-RSRG demonstrate the challenges of multi-entity reasoning and verify the effectiveness of our proposed EAR framework. Our dataset, code, and models will be available at https://github.com/CV-ShuchangLyu/ME-RSRG.

cs.CV↗

arXiv2026

Global Rotation Equivariant Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction

While deep learning has advanced speech enhancement (SE), effective phase modeling remains challenging, as conventional networks typically operate within a flat Euclidean feature space, which is not easy to model the underlying circular topology of the phase. To address this, we propose a magnitude-phase dual-stream framework that aligns the phase stream with its intrinsic circular geometry by enforcing Global Rotation Equivariance (GRE) characteristic. Specifically, we introduce a Magnitude-Phase Interactive Convolutional Module (MPICM) for modulus-based information exchange and a Hybrid-Attention Dual Feed-Forward Network (HADF) bottleneck for unified feature fusion, both of which are designed to preserve GRE in the phase stream. Comprehensive evaluations are conducted across phase retrieval, denoising, dereverberation, and bandwidth extension tasks to validate the superiority of the proposed method over multiple advanced baselines. Notably, the proposed architecture reduces Phase Distance by over 20\% in the phase retrieval task and improves PESQ by more than 0.1 in zero-shot cross-corpus denoising evaluations. The overall superiority is also established in universal SE tasks involving mixed distortions. Qualitative analysis further reveals that the learned phase features exhibit distinct periodic patterns, which are consistent with the intrinsic circular nature of the phase. The source code is available at https://github.com/wangchengzhong/GRE-Net.

cs.SD↗

arXiv2025

Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey

Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has broad applications across various real-world applications, including remote sensing element interpretation, ecological environment monitoring, and urban/rural planning. However, domain adaptation in remote sensing poses significant challenges due to differences in data, such as variations in ground sampling distance, imaging modes from various sensors, geographical landscapes, and environmental conditions. In recent years, deep learning has emerged as a powerful tool for feature representation and cross-domain knowledge transfer, leading to widespread adoption in remote sensing tasks. In this paper, we present a comprehensive survey of significant advancements in deep learning based domain adaptation for remote sensing. We first introduce the preliminary knowledge to clarify key concepts, mathematical notations, and the taxonomy of methodologies. We then organize existing algorithms from multiple perspectives, including task categorization, input mode, supervision paradigm, and algorithmic granularity, providing readers with a structured understanding of the field. Next, we review widely used datasets and summarize the performance of state-of-the-art methods to provide an overview of current progress. We also identify open challenges and potential directions to guide future research in domain adaptation for remote sensing. Compared to previous surveys, this work addresses a broader range of domain adaptation tasks in remote sensing, rather than concentrating on a few subfields. It also presents a systematic taxonomy, providing a more comprehensive and organized understanding of the field. As a whole, this survey can inspire the research community, foster understanding, and guide future work in the field.

cs.CV↗

arXiv2024

GALD-SE: Guided Anisotropic Lightweight Diffusion for Efficient Speech Enhancement

Speech enhancement is designed to enhance the intelligibility and quality of speech across diverse noise conditions. Recently, diffusion model has gained lots of attention in speech enhancement area, achieving competitive results. Current diffusion-based methods blur the signal with isotropic Gaussian noise and recover clean speech from the prior. However, these methods often suffer from a substantial computational burden. We argue that the computational inefficiency partially stems from the oversight that speech enhancement is not purely a generative task; it primarily involves noise reduction and completion of missing information, while the clean clues in the original mixture do not need to be regenerated. In this paper, we propose a method that introduces noise with anisotropic guidance during the diffusion process, allowing the neural network to preserve clean clues within noisy recordings. This approach substantially reduces computational complexity while exhibiting robustness against various forms of noise and speech distortion. Experiments demonstrate that the proposed method achieves state-of-the-art results with only approximately 4.5 million parameters, a number significantly lower than that required by other diffusion methods. This effectively narrows the model size disparity between diffusion-based and predictive speech enhancement approaches. Additionally, the proposed method performs well in very noisy scenarios, demonstrating its potential for applications in highly challenging environments.

cs.SD↗

arXiv2024

StreamAAD: Decoding Spatial Auditory Attention with a Streaming Architecture

In this paper, we present our approach for the Track 1 of the Chinese Auditory Attention Decoding (Chinese AAD) Challenge at ISCSLP 2024. Most existing spatial auditory attention decoding (Sp-AAD) methods employ an isolated window architecture, focusing solely on global invariant features without considering relationships between different decision windows, which can lead to suboptimal performance. To address this issue, we propose a novel streaming decoding architecture, termed StreamAAD. In StreamAAD, decision windows are input to the network as a sequential stream and decoded in order, allowing for the modeling of inter-window relationships. Additionally, we employ a model ensemble strategy, achieving significant better performance than the baseline, ranking First in the challenge.

cs.SD↗

arXiv2024

Enhancing spatial auditory attention decoding with neuroscience-inspired prototype training

The spatial auditory attention decoding (Sp-AAD) technology aims to determine the direction of auditory attention in multi-talker scenarios via neural recordings. Despite the success of recent Sp-AAD algorithms, their performance is hindered by trial-specific features in EEG data. This study aims to improve decoding performance against these features. Studies in neuroscience indicate that spatial auditory attention can be reflected in the topological distribution of EEG energy across different frequency bands. This insight motivates us to propose Prototype Training, a neuroscience-inspired method for Sp-AAD. This method constructs prototypes with enhanced energy distribution representations and reduced trial-specific characteristics, enabling the model to better capture auditory attention features. To implement prototype training, an EEGWaveNet that employs the wavelet transform of EEG is further proposed. Detailed experiments indicate that the EEGWaveNet with prototype training outperforms other competitive models on various datasets, and the effectiveness of the proposed method is also validated. As a training method independent of model architecture, prototype training offers new insights into the field of Sp-AAD.

cs.HC↗

arXiv2022

A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio

This letter proposes a new blind source separation (BSS) framework termed minimum variance independent component analysis (MVICA), which can potentially achieve the maximum output signal-to-interference ratio (SIR) while also allowing more flexibility in real implementations. The statistical independence assumption has been the foundation of the most dominant BSS techniques in recent decades. However, this assumption does not always hold true and the accurate probabilistic modeling of source is inherently difficult. To overcome these limitations and improve the separation performance, the MVICA framework is rigorously derived by optimizing the design of these independence-based BSS algorithms with the maximum SIR criterion. A deep neural network-supported implementation of MVICA is subsequently described. Experimental results under various conditions show the superiority of MVICA over the state-of-the-art BSS algorithms, in terms of not only SIR but also signal-to-distortion ratio and automatic speech recognition rate.

cs.SD↗