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cs.CV2026
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction
Zichun Guo, Yuling Shi, Wenhao Zeng +6
Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabilities are mainly evaluated on p…
cs.CV2024
Composition Vision-Language Understanding via Segment and Depth Anything Model
Mingxiao Huo, Pengliang Ji, Haotian Lin +3
We introduce a pioneering unified library that leverages depth anything, segment anything models to augment neural comprehension in language-vision model zero-shot understanding. T…
cs.CV2024
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
Yuning Wang, Zhiyuan Liu, Haotian Lin +3
Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driv…