28 accepted papers
Multimodal fake news detection plays a crucial role in combating online misinformation. The inherent domain diversity of news in the real world has driven the development of cross-domain detection methods. However, these detection methods either suffer from significant performance degradation due to
Cable driven parallel robot (CDPR) possesses large workspace, outstanding dynamic performance and high load capacity. However, due to the actuation method of CDPR, only limited motion modes can be achieved, and its rotational and translational motions are usually coupled. This letter presents a nove
Large Reasoning Models (LRMs) have recently demonstrated impressive performance across a range of reasoning tasks by generating intermediate thoughts. However, these models can suffer from overthinking—generating excessive tokens that contribute little to final accuracy while increasing inference co
Existing audio adversarial attack methods suffer from poor transferability, primarily due to insufficient exploration of model decision mechanisms and overreliance on heuristic-driven algorithm design. This paper aims to alleviate this gap. Specifically, through observations across three mainstream
Recent advances in vision-language models (VLMs) have shown remarkable performance in medical image classification tasks. However, applying VLMs to fetal cardiac ultrasound (FCU) remains challenging due to compound distribution shifts, including covariate shifts caused by cross-center heterogeneity
The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often limited to single 12-lead, short-duration (10s) ECG inputs, thereby underutilizi
Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, VLMs still show regional biases in this task. To systematically evaluate these issues, we introduce a benchmark consisting of 1,200 images pa
Weakly-supervised Temporal Action Localization (WTAL) has achieved notable success but still suffers from a lack of temporal annotations, leading to a performance and framework gap compared with fully-supervised methods. While recent approaches employ pseudo labels for training, three key challenges…
Pathological image has been recognized as the gold standard for cancer diagnosis for more than a century. However, some internal regions of pathological images may inevitably exhibit various degradation issues, including low resolution, image blurring, and image noising, which will affect disease di…
Real-time dense depth completion has become a critical enabling technology for autonomous systems requiring instant environment perception, such as self-driving vehicles and robotics. While recent depth completion methods achieve remarkable accuracy, their computational complexity often exceeds the
In this work, we investigate the problem of adapting to the presence or absence of causal structure in multi-armed bandit problems. In addition to the usual reward signal, we assume the learner has access to additional variables, observed in each round after acting. When these variables $d$-separate…
Large language models (LLMs) have demonstrated the potential to mimic human social intelligence. However, most studies focus on simplistic and static self-report or performance-based tests, which limits the depth and validity of the analysis. In this paper, we developed a novel framework, InterInten…
The construction of large-scale space facilities requires the use of on-orbit construction technology. However, several of its key components, such as standard interface design, compliant control methods, and path planning for multi-branch robots, still need improvement before practical application.…
In a plethora of recent work, large language models (LLMs) demonstrated impressive reasoning ability, but many proposed downstream reasoning tasks only focus on performance-wise evaluation. Two fundamental questions persist: 1) how consistent is the reasoning, and 2) can models detect unreliable rea…
We study the fundamental problem of sequential probability assignment, also known as online learning with logarithmic loss, with respect to an arbitrary, possibly nonparametric hypothesis class. Our goal is to obtain a complexity measure for the hypothesis class that characterizes the minimax regret…
Among the remarkable emergent capabilities of large language models (LMs) is free-text rationalization; beyond certain scale, large LMs are capable of generating seemingly useful rationalizations, which in turn, can dramatically enhance their performances on leaderboards. This phenomenon raises a qu…
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert supervision, in the form of demonstrations or rich reward functions, to lea…
Federated Learning (FL) aims to achieve a global model via aggregating models from all devices. However, it can diverge when the data on the users’ devices are heterogeneous. To address this issue, we propose a novel clustered FL method (FPFC) based on a nonconvex pairwise fusion penalty. FPFC can a…
Deep models have the risk of utilizing spurious clues to make predictions, e.g., recognizing actions via classifying the background scene. This problem severely degrades the open-set action recognition performance when the testing samples exhibit scene distributions different from the training sampl…
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers. The OCR model easily gets confused on recognizing handwritten Chinese characters, and the textual information of the answers is missing during the mo…
By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (NLMs). Explanation regularization (ER) aims to improve NLM generalization by pushing the NLM’s machine rationales (Which input tokens did the NLM focus on?) to align wit…
A common approach to Temporal Action Localization (TAL) is to generate action proposals and then perform action classification and localization on them. For each proposal, existing methods universally use a shared proposal-level representation for both tasks. However, our analysis indicates that thi…
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…
Collision-free motion planning allows robots to perform tasks in complex environments, but the conservative bounding curves during collision detection will reduce the robot's dexterity. This paper presents a novel algorithm for real-time motion planning of non-holonomic robots in dynamic scenes. To
The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foregroun…
RNA-binding proteins (RBPs) play a significant part in several biological processes in the living cell, such as gene regulation and mRNA localization. Several deep learning methods, especially the model based on convolutional neural network(CNN), have been used to predict the binding sites. However,…
Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to localize temporal starts and ends of action instances in a video under only video-level supervision. Existing WS-TAL methods rely on deep features learned for action recognition. However, due to the mismatch between classificat…
Weakly-supervised temporal action localization (WS-TAL) is a promising but challenging task with only video-level action categorical labels available during training. Without requiring temporal action boundary annotations in training data, WS-TAL could possibly exploit automatically retrieved video…