13 accepted papers
To address the challenge of achieving both low drag and high maneuverability in complex water-filled pipeline environments, this study proposes a novel dual-spherical pipeline robot with integrated leak detection and mapping capabilities. A multi-objective optimization framework was established to s…
In personalized federated learning (PFL), collaboration graphs specify model aggregation among clients. However, without constraints on the collaboration geometry, training can drift into two degenerate regimes: global consensus or spontaneous clustering. This paper provides a unified dynamical anal…
Large language models fine-tuned on instruction–code pairs may memorize and subsequently leak sensitive training data. Existing differentially private (DP) code generation methods primarily protect code snippets while assuming prompts are public, which fails in realistic scenarios where prompts may …
Despite the advanced capabilities of Large Vision-Language Models (LVLMs), they frequently suffer from object hallucination. One reason is that visual features and pretrained textual representations often become intertwined in the deeper network layers. To address this, we propose REVIS, a training-…
LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering. Despite the high cost of creating these datasets, existing literature has overlooked copyr…
Behavior Trees (BTs) are a widely used control architecture in robotics, renowned for their robustness and safety, which are especially crucial for everyday service robots. Recently, several methods have been proposed to automatically plan BTs to accomplish specific tasks. However, existing research
Recent approaches, such as data augmentation, adversarial training, and transfer learning, have shown potential in addressing the issue of performance degradation caused by distributional shifts. However, they typically demand careful design in terms of data or models and lack awareness of the impac…
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialo…
Empathetic response generation endeavors to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent…
The performance of deep learning models often degrades rapidly when faced with imbalanced data characterized by a long-tailed distribution. Researchers have found that the fully connected layer trained by cross-entropy loss has large weight-norms for classes with many samples, but not for classes wi…
Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, l…
Recently, the robustness of deep neural networks has drawn extensive attention due to the potential distribution shift between training and testing data (e.g., deep models trained on high-quality images are sensitive to corruption during testing). Many researchers attempt to make the model learn inv…
"Despite the remarkable performance on high-quality (HQ) data, the accuracy of deep image recognition models degrades rapidly in the presence of low-quality (LQ) images. Both feature de-drifting and quality agnostic models have been developed to make the features extracted from degraded images close…