17 accepted papers
Medical image quality assessment (IQA) serves as a critical safeguard for precise clinical diagnosis and treatment. However, existing methods still face challenges arising from data scarcity and heterogeneity across imaging domains, which confine solutions to domain-specific designs and limit their
Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos but often suffers from severe performance degradation under domain shifts. Traditional STMap-based methods [??] rely on predefined spatio-temporal representations that offer engineered robustness but
High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequ
Accurate prediction of breast cancer recurrence after treatment is essential for improving long-term outcomes. However, existing models are limited by three key challenges: (1) they typically rely on single-modal data, missing cross-modal interactions; (2) they analyze static snapshots, failing to c
This paper proposes a variant of multiple-play stochastic bandits tailored to resource allocation problems arising from LLM applications, edge intelligence, etc. The model is composed of finite number of arms and plays. Each arm has a stochastic number of capacities, and each unit of capacity is ass
Remote photoplethysmography (rPPG) measurement enables non-contact physiological monitoring but suffers from accuracy degradation under head motion and illumination changes. Existing deep learning methods are mostly heuristic and lack theoretical grounding, limiting robustness and interpretability.
Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. Large Language Models (LLMs) excel at capturing long-range dependencies, offering a potential solution but struggl…
Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs underst
The end-to-end image dehazing network relies on paired training data. However, there is limited training data available for real non-homogeneous dehazing, which limits the performance of dehazing networks on real non-homogeneous hazy images. To overcome this limitation, we propose a method to augmen…
Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct…
Multi-object images are widely present in the real world, spanning various areas of daily life. Efficient and accurate editing of these images is crucial for applications such as augmented reality, advertisement design, and medical imaging. Stable Diffusion (SD) has ushered in a new era of high-qual…
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propo…
This paper revisits the estimation bias control problem of Q-learning, motivated by the fact that the estimation bias is not always evil, i.e., some environments benefit from overestimation bias or underestimation bias, while others suffer from these biases. Different from previous coarse-grained b…
With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction.However, there is a scarcity of benchmarks available for LLM-based mobile agents.Benchmarking these agents generally faces three main challenges:(1) The ine…
Recently, mobile AI agents based on VLMs have been gaining increasing attention. These works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile datasets. However, these VLMs are typically pre-trained on general-domain data, which often results in a lack of fundamenta…
Recently, self-supervised 6D object pose estimation, where synthetic images with object poses (sometimes jointly with un-annotated real images) are used for training, has attracted much attention in computer vision. Some typical works in literature employ a time-consuming differentiable renderer for…
We propose a new learning paradigm, Local to Global Learning (LGL), for Deep Neural Networks (DNNs) to improve the performance of classification problems. The core of LGL is to learn a DNN model from fewer categories (local) to more categories (global) gradually within the entire training set. LGL i…