Papers with AVG

2 papers
HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction (2023.findings-emnlp)

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Challenge: Existing approaches to extract sentence-level features are labor-intensive and time-consuming.
Approach: They propose a distantly supervised relation extraction algorithm that uses circular cosine similarity to show intrinsic associations between sentences within a bag.
Outcome: The proposed method outperforms baselines on the popular DSRE datasets.
AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity (2025.findings-acl)

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Challenge: Existing large multimodal models typically divide high-resolution images into multiple local images and a global image, leading to a large number of visual tokens.
Approach: They propose an LMM that can adaptively select the appropriate visual granularity based on the input image and instruction.
Outcome: The proposed model significantly reduces visual tokens and speeds up inference on 11 benchmarks.

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