13 accepted papers
Multi-view evidential learning aims to integrate information from multiple views to improve prediction performance and provide trustworthy uncertainty estimation. Most previous methods assume that view-specific evidence learning is naturally reliable. However, in practice, the evidence learning proc…
Real-world deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unreliable, posing severe risks in safety-critical applications. Existing methods improve calibration via training-time regularization or post-hoc adjustment, but often …
Despite the progress in text-to-image generation, semantic image editing remains a challenge. Inversion-based algorithms unavoidably introduce reconstruction errors, while instruction-based models mainly suffer from limited dataset quality and scale. To address these problems, we propose a descripti…
Many-shot in-context learning has recently shown promise as an alternative to finetuning, with the major advantage that the same model can be served for multiple tasks. However, this shifts the computational burden from training-time to inference-time, making deployment of many-shot ICL challenging…
In recent years, audio-driven neural radiance field (NeRF)-based talking head generation techniques have achieved impressive results. However, these methods still have some limitations, such as unsynchronized lip movements and visual jitter. Recently, 3D Gaussian splatting has gradually replaced NeR…
In the field of sign language generation, Transformer-based models have been widely studied, but their quadratic computational complexity poses challenges. State Space Models (SSMs), like Mamba, offer a promising alternative with efficient long-range interaction modeling and linear complexity. In th…
The emerging diffusion models (DMs) have demonstrated the remarkable capability of generating images via learning the noised score function of the data distribution. Current DM sampling techniques typically rely on first-order Langevin dynamics at each noise level, with efforts concentrated on refin…
Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve factuality. However, a critical yet underexplored aspect of RAG pipelines is chunking—the process of dividing documents into retrievable units. Existin
"The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based methods have limitations in urban scenes due to reliance on initial Structure-from-Motion (SfM) points and difficulties in…
Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critical applications. To address this issue, researchers propose trusted multi-view methods that learn the class distribution…
The reactor power and the coolant level in the nuclear plant are monitored via the reactor core detectors. Every 4 to 5 years, the detectors with high-level radiation need to be removed, which is time-consuming and hazardous for workers. To address this issue, this paper introduces a novel robotic s…
This paper uses the relationship between graph conductance and spectral clustering to study (i) the failures of spectral clustering and (ii) the benefits of regularization. The explanation is simple. Sparse and stochastic graphs create several
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints. Often, identifying weak (but scientifically interesting) associations between a set of predictors and a response necessitates pooling datasets from multiple diverse labs or groups. Whi…