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The Hong Kong University of Science and Technology (Guangzhou)
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MLLMs can now achieve over 8% improvement in spatial reasoning for egocentric scenes by leveraging a novel Ego-element Graph for enhanced perception.
LVLMs can achieve SOTA visual reasoning by learning to "see" in a way that optimizes for reasoning, even if it means deviating from strict geometric accuracy.
Doubling the number of tokens in a ViT-based autoencoder, combined with staged compression and self-supervised pretraining, dramatically improves generative performance under deep compression, without increasing the latent budget.
Current MLLMs are surprisingly bad at understanding human intent in egocentric videos at a step-by-step level, achieving only 33% accuracy on a new benchmark designed to prevent future-frame leakage.
The first comprehensive survey of Visual Document Retrieval reveals how MLLMs are reshaping the field, highlighting the shift towards RAG and agentic systems for complex document understanding.
