12 accepted papers
With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security. Despite remarkable progress in existing deepfake detection methods, AIGC forgery detection re
The rapid development of large language models (LLMs) has relied on access to high-quality, large-scale datasets, yet growing concerns around data privacy and security have spurred substantial research into pre-training data detection. While state-of-the-art (SOTA) methods such as RECALL and CON-REC
Multimodal fusion of color fundus photography (CFP) and optical coherence tomography (OCT) B-scan images has demonstrated superior diagnostic potential for retinal diseases compared to single-modality approaches. However, existing fusion paradigms - whether through naive concatenation or attention m
Multi-view diabetic retinopathy (DR) grading has achieved remarkable performance by capturing more comprehensive pathological features than single-view methods. However, complete multi-view fundus images are often difficult to obtain in clinical practice, and the performance degrades significantly w
Large language models (LLMs) are seeing growing adoption in multi-agent systems. In these systems, efficient failure attribution is critical for ensuring robustness and interpretability. Current LLM-based attribution methods often face challenges with lengthy logs and lacking expert knowledge. Drawi
As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fun
Deep neural networks (DNNs) have significantly advanced diabetic retinopathy (DR) diagnosis, yet their black-box nature limits clinical acceptance due to a lack of interpretability. Concept bottleneck model (CBM) offers a promising solution by enabling concept-level reasoning and test-time intervent
With the advancement of computer vision, numerous models have been proposed for screening of fundus diseases. However, the recognition of multiple fundus diseases is often hampered by the simultaneous presence of multiple disease types and the confluence of lesion types in fundus images. This paper…
This paper addresses the gap in predicting turn-taking and backchannel actions in human-machine conversations using multi-modal signals (linguistic, acoustic, and visual). To overcome the limitation of existing datasets, we propose an automatic data collection pipeline that allows us to collect and…
Training drones to execute complex collective tasks via multi-agent reinforcement learning presents significant challenges. To address these challenges, this letter introduces the Task-Activated Language model-based Knowledge-Extension Reasoning system. Specifically, we trained drones in two fine-gr
There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock rela…
We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple…