15 accepted papers
The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesiology remain underexplored. To bridge this gap, we introduce AnesSuite, the first comprehensive dataset suite specifical…
Existing diffusion-based object removal and inpainting methods often fail to recover the fine structural and textural details of small objects. This is primarily due to the VAE encoder’s downsampling, which inevitably compresses small masked regions and causes significant detail loss, while the deco…
Recently, diffusion policy models have been applied in the field of robotics. Most existing methods use all observations as condition inputs to the diffusion model. With the denoising of the diffusion model, Gaussian noise gradually becomes an action sequence. However, since these constraints are im
Monocular image-goal navigation in an outdoor environment is a challenging task. Robots have to face monocular scale uncertainty and complex environments. Recently, implementations based on imitation learning have made significant progress. However, robots tend to focus too much on the current state
Tree of Thoughts (ToT) enhances Large Language Model (LLM) reasoning by structuring problem-solving as a spanning tree. However, recent methods focus on search accuracy while overlooking computational efficiency. The challenges of accelerating the ToT lie in the frequent switching of reasoning focus…
The geometry of terrain is crucial for developing terrain-aware locomotion policies. Recent advancements in quadrupedal locomotion based on learning rely on depth information obtained from LiDARs and depth cameras. Despite the capabilities of these locomotion policies on terrains, they pose challeng
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teacher…
This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning thoughts within examples used for in-context learning. We construct NoRa dataset that is tailored to evaluate the ro…
Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in…
Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit in the domain of robotics, which facilitates robots in executing object manipulations based on human language directives. To achieve this, some research efforts have been dedicated to the development o
Generalized Zero-Shot Learning (GZSL) methods often assume that the unseen classes are similar to seen classes and thus perform poor when unseen classes are dissimilar to seen classes. Although some existing GZSL approaches can alleviate this issue by leveraging additional semantic information from…
Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtai…
Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. However, existing methods genera…
Protein subcellular localization(PSL) is an important task to study human cell functions and cancer pathogenesis. It has attracted great attention in the computer vision community. However, the huge size of immune histochemical (IHC) images, the disorganized location distribution in different tissue…
Multi-source domain adaptation has attracted great attention in machine learning community. Most of these methods focus on weighting the predictions produced by the adaptation networks of different domains. Thus the domain shifts between certain of domains and target domain are not effectively relie…