Papers with image embedding

3 papers
Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)

Copied to clipboard

Challenge: Recent research focuses on improving prediction performance and reliability of LLM.
Approach: They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM.
Outcome: The proposed method improves performance on knowledge-based VQA benchmarks.
Bridging Semantic and Modality Gaps in Zero-Shot Captioning via Retrieval from Synthetic Data (2025.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for zero-shot image captioning fail to bridge the semantic gap between images and textual inputs.
Approach: They propose a retrieval-based framework that leverages only existing synthetic image-text pairs as its search corpus to bridge the gap when using synthetic data for captioning.
Outcome: The proposed method bridges the semantic gap between a synthetic image and its input text . it extracts image-related textual descriptions to mitigate the modality gap during decoding .
"Penny Wise, Pixel Foolish": Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations (2026.findings-acl)

Copied to clipboard

Challenge: Mobile Agents are a key component of the “Agentic Economy” where they perform high-stakes financial transactions.
Approach: They propose a systemic vulnerability termed Visual Dominance Hallucination (VDH) VDH exploits the modality gap in CLIP-based encoders via a novel Semantic-Decoupling Loss.
Outcome: The proposed framework exploits the modality gap in CLIP-based encoders by preserving fidelity.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations