14 accepted papers
Anomaly detection platforms in real-world environments require continuous interaction between automated systems and domain experts, as anomalies evolve dynamically and their definitions vary across contexts. Therefore, an effective platform must collaborate with experts and incorporate their feedbac
Large Language Models (LLMs) have emerged as powerful tools for semantic reasoning, enabling the formalization of tasks that traditionally relied on manual human intuition. This capability extends to environment design in Reinforcement Learning (RL). While prior research predominantly focuses on rew…
Multi-agent debate has shown promise for improving the reasoning of large language models, yet recent theory suggests its benefits are highly regime-dependent. While interaction can amplify informative signals under corrective conditions, symmetric debate dynamics are neutral in expectation, often m…
Nuclei instance segmentation and classification are fundamental but remain challenging in pathology due to severe class imbalance and organ- and stain-induced variability. While vision-language approaches can inject explicit semantic cues that reduce spurious contextual bias under imbalance, the abs
Causal agents have emerged as promising tools for automating causal analysis based on user queries. However, existing causal agent systems are often limited to a single causal task, limiting their ability to handle complex queries. In addition, they accept only numerical data as input, preventing th
Industrial automation in smart logistics and factories requires simulation platforms that support rapid environment building before costly physical deployment. Yet existing tools often require substantial expertise, complex setup, and long configuration times, hindering agile prototyping. We present
Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables effi
In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descrip…
In this work, we tackle action-scene hallucination in Video Large Language Models (Video-LLMs), where models incorrectly predict actions based on the scene context or scenes based on observed actions. We observe that existing Video-LLMs often suffer from action-scene hallucination due to two main fa…
The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One str…
"3D dense captioning is a task involving the localization of objects and the generation of descriptions for each object in a 3D scene. Recent approaches have attempted to incorporate contextual information by modeling relationships with object pairs or aggregating the nearest neighbor features of an…
3D dense captioning is a task to localize objects in a 3D scene and generate descriptive sentences for each object. Recent approaches in 3D dense captioning have adopted transformer encoder-decoder frameworks from object detection to build an end-to-end pipeline without hand-crafted components. Howe…
Optical Chemical Structure Recognition (OCSR) deals with the translation from chemical images to molecular structures, this being the main way chemical compounds are depicted in scientific documents. Traditionally, rule-based methods have followed a framework based on the detection of chemical entit…
Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to ma…