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Guokuan Li

5 accepted papers

2026

TRIAGE: HIERARCHICAL VISUAL BUDGETING FOR EFFICIENT VIDEO REASONING IN VISION-LANGUAGE MODELS

ICASSP 2026oral

Vision-Language Models (VLMs) face significant computational challenges in video processing due to massive data redundancy, which creates prohibitively long token sequences. To address this, we introduce Triage, a training-free, plug-and-play framework that reframes video reasoning as a resource all…

Cited by 0SourcePDFScholar
2026

Vista: Scene-Aware Optimization for Streaming Video Question Answering Under Post-Hoc Queries

AAAI 2026technical

Streaming video question answering (Streaming Video QA) poses distinct challenges for multimodal large language models (MLLMs), as video frames arrive sequentially and user queries can be issued at arbitrary timepoints. Existing solutions relying on fixed-size memory or naive compression often suffe

Cited by 0SourcePDFScholar
2025

RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models

ACL 2025finding

Object Navigation (ObjectNav) is a fundamental task in embodied artificial intelligence. Although significant progress has been made in semantic map construction and target direction prediction in current research, redundant exploration and exploration failures remain inevitable. A critical but unde…

Cited by 0SourcePDFScholar
2025

RUNA: Object-Level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations

AAAI 2025technical

Enabling object detectors to recognize out-of-distribution (OOD) objects is vital for building reliable systems. A primary obstacle stems from the fact that models frequently do not receive supervisory signals from unfamiliar data, leading to overly confident predictions regarding OOD objects. Despi…

Cited by 0SourcePDFScholar
2025

VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection

ICASSP 2025accepted

As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original training data, the challenge of zero-shot object-level out-of-distribution (OOD) detection arises. This task becomes crucial in ensuring the reliability of detectors…

Cited by 0SourceScholar