Papers with average pooling

3 papers
InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution (2023.findings-emnlp)

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Challenge: a recent study shows that multi-modal data representations tend to cluster within a limited convex cone, hindering retrieval performance.
Approach: They propose a method that uses graph convolution and average pooling to separate representations by increasing distances between data points.
Outcome: The proposed method reduces representation degeneration problem by increasing distances between data points.
Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product (2025.naacl-long)

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Challenge: Existing methods for fine-tuning pre-trained language models overlook intrinsic semantic associations between soft prompt tokens, leading to high discreteness and limited interactions.
Approach: They propose a low-parameters Prompt Tuning method which leverages prompt decomposition and compressed outer product to facilitate multiple interactions among prompt tokens.
Outcome: Experiments on six architectures and eight datasets show that the proposed method outperforms state-of-the-art methods in performance and efficiency.
Information Aggregation via Dynamic Routing for Sequence Encoding (C18-1)

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Challenge: Currently, little attention is paid to how to aggregate text sequences into fixed-size vectors.
Approach: They propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy.
Outcome: The proposed method outperforms other aggregating methods on five text classification tasks.

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