CLSC DETR: Reliable Candidate Ranking via Cross Layer Geometric Support for UAV Small Object Detection
针对无人机小目标检测中的可靠候选排序问题，提出CLSC DETR方法，通过跨层几何支持和一致性校准提高定位质量估计和候选排序的稳定性。
0 citationsRead paper
Selected work
Representative Papers
Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection
This work addresses the limitations of existing remote sensing change detection methods, which typically rely on one-shot dense prediction and overlook the frequency characteristics of changes, leading to poor robustness under complex appearance variations and noise. To overcome this, the paper reformulates change detection as a structured generative task in the frequency domain and introduces a next-frequency autoregressive modeling paradigm that progressively generates change maps from coarse to fine scales. Key innovations include a frequency-aware masked tokenization strategy, a scale-aligned RoPE cross-attention module, and a change quality control mechanism. Built upon Fourier transform and quantized decomposition, the proposed Frequency VAR Transformer integrates dynamic normalization with spatial-frequency alignment. Extensive experiments on CDD, GZ-CD, and LEVIR-CD benchmarks demonstrate significant performance gains over state-of-the-art methods, particularly exhibiting superior robustness in complex scenarios.
0 citationsRead paper
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
This work addresses the exacerbated Matthew effect in conversational recommender systems caused by dynamic user-item interactions. To mitigate popularity bias and enhance long-tail item coverage, the authors propose a novel multi-channel hypergraph architecture that jointly models items, knowledge graph entities, and words to construct a multi-granular representation of user interests. By integrating a multi-interest self-supervised learning mechanism, the approach effectively balances recommendation accuracy with fairness. Extensive experiments on four standard conversational recommendation benchmarks demonstrate that the proposed method consistently achieves state-of-the-art performance, significantly improving both recommendation fairness and effectiveness for long-tail items.
0 citationsRead paper
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
This work addresses the Matthew effect in conversational recommender systems—where frequent interactions lead to overexposure of popular items and neglect of niche ones—by proposing a multi-preference representation learning framework that integrates five dimensions: items, entities, words, reviews, and knowledge. For the first time, hypergraph neural networks are introduced to model high-order correlations among these heterogeneous signals, enabling joint optimization of dialogue generation and recommendation prediction. Evaluated on two benchmark datasets, the proposed method significantly outperforms existing approaches, achieving superior recommendation accuracy while effectively mitigating the Matthew effect and balancing fairness with predictive performance.
0 citationsRead paper
How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design
This work addresses the inefficiency in action selection when large language models (LLMs) automatically generate heuristics under limited query and evaluation budgets. To overcome this challenge, the authors propose a dual-agent guided action selection mechanism: a transition agent models the generative distribution of latent representations of sub-heuristics, while a utility agent predicts their performance on specific problem instances. An acquisition strategy that integrates the uncertainties of both agents dynamically guides the selection of parent heuristics and generation operators. This approach transcends the limitations of traditional rule-based or static-preference methods, achieving performance that matches or surpasses strong existing baselines across multiple heuristic design tasks. Ablation studies further confirm the effectiveness and non-trivial contribution of the proposed mechanism.
0 citationsRead paper
Recent publications
Latest Papers
CLSC DETR: Reliable Candidate Ranking via Cross Layer Geometric Support for UAV Small Object Detection
针对无人机小目标检测中的可靠候选排序问题，提出CLSC DETR方法，通过跨层几何支持和一致性校准提高定位质量估计和候选排序的稳定性。
0 citationsRead paper
Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection
This work addresses the limitations of existing remote sensing change detection methods, which typically rely on one-shot dense prediction and overlook the frequency characteristics of changes, leading to poor robustness under complex appearance variations and noise. To overcome this, the paper reformulates change detection as a structured generative task in the frequency domain and introduces a next-frequency autoregressive modeling paradigm that progressively generates change maps from coarse to fine scales. Key innovations include a frequency-aware masked tokenization strategy, a scale-aligned RoPE cross-attention module, and a change quality control mechanism. Built upon Fourier transform and quantized decomposition, the proposed Frequency VAR Transformer integrates dynamic normalization with spatial-frequency alignment. Extensive experiments on CDD, GZ-CD, and LEVIR-CD benchmarks demonstrate significant performance gains over state-of-the-art methods, particularly exhibiting superior robustness in complex scenarios.
0 citationsRead paper
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
This work addresses the exacerbated Matthew effect in conversational recommender systems caused by dynamic user-item interactions. To mitigate popularity bias and enhance long-tail item coverage, the authors propose a novel multi-channel hypergraph architecture that jointly models items, knowledge graph entities, and words to construct a multi-granular representation of user interests. By integrating a multi-interest self-supervised learning mechanism, the approach effectively balances recommendation accuracy with fairness. Extensive experiments on four standard conversational recommendation benchmarks demonstrate that the proposed method consistently achieves state-of-the-art performance, significantly improving both recommendation fairness and effectiveness for long-tail items.
0 citationsRead paper
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
This work addresses the Matthew effect in conversational recommender systems—where frequent interactions lead to overexposure of popular items and neglect of niche ones—by proposing a multi-preference representation learning framework that integrates five dimensions: items, entities, words, reviews, and knowledge. For the first time, hypergraph neural networks are introduced to model high-order correlations among these heterogeneous signals, enabling joint optimization of dialogue generation and recommendation prediction. Evaluated on two benchmark datasets, the proposed method significantly outperforms existing approaches, achieving superior recommendation accuracy while effectively mitigating the Matthew effect and balancing fairness with predictive performance.
0 citationsRead paper
How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design
This work addresses the inefficiency in action selection when large language models (LLMs) automatically generate heuristics under limited query and evaluation budgets. To overcome this challenge, the authors propose a dual-agent guided action selection mechanism: a transition agent models the generative distribution of latent representations of sub-heuristics, while a utility agent predicts their performance on specific problem instances. An acquisition strategy that integrates the uncertainties of both agents dynamically guides the selection of parent heuristics and generation operators. This approach transcends the limitations of traditional rule-based or static-preference methods, achieving performance that matches or surpasses strong existing baselines across multiple heuristic design tasks. Ablation studies further confirm the effectiveness and non-trivial contribution of the proposed mechanism.
0 citationsRead paper