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Visual Large Language Models for Generalized and Specialized Applications
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Visual Large Language Models for Generalized and Specialized Applications
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Visual-language models (VLM) have emerged as a powerful tool for learning a unified embedding space for vision and language. Inspired by large language models, which have demonstrated strong reasoning and multi-task capabilities, visual large language models (VLLMs) are gaining increasing attention for building general-purpose VLMs. Despite the significant progress made in VLLMs, the related literature remains limited, particularly from a comprehensive application perspective, encompassing generalized and specialized applications across vision (image, video, depth), action, and language modalities. In this survey, we focus on the diverse applications of VLLMs, examining their using scenarios, identifying ethics consideration and challenges, and discussing future directions for their development. By synthesizing these contents, we aim to provide a comprehensive guide that will pave the way for future innovations and broader applications of VLLMs. The paper list repository is available: https://github.com/JackYFL/awesome-VLLMs.
Forward citations
Cited by 7 Pith papers
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GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning
GaLa uses hypergraph representations of objects and a TriView encoder with contrastive learning to improve vision-language models on procedural planning benchmarks.
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AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models
AVA-Bench evaluates vision foundation models by disentangling 14 atomic visual abilities with aligned training-test distributions to reveal precise ability fingerprints.
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Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
CoT reasoning is a brittle mirage governed by distribution discrepancy between training and test data, demonstrated via controlled experiments in the new DataAlchemy environment.
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Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making
Penalizing off-prior attribution evidence during training with subset-selection attribution improves accuracy and attribution reasonability in image classifiers and MLLM-based GUI agents.
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Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making
A training approach that adds attribution constraints derived from human priors to steer models toward using specified input regions for decisions.
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IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
On IndustryNav, a dynamic Unity warehouse navigation benchmark, nine VLLMs earned only 4.9–65.3% success and high collision/warning rates, with closed-source models ahead.
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Lost in Cultural Translation: Do LLMs Struggle with Math Across Cultural Contexts?
LLMs show accuracy drops of 0.3% to 5.9% on GSM8K math problems when culturally adapted to six countries while keeping math operations identical, with statistical significance confirmed by McNemar tests.
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