4 citations · 7 across the 14 of their papers we have counts for
11 papers · 1 filter
Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
Yunxin Li, Xinyu Chen, Shenyuan Jiang +9
We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
Yunxin Li, Baotian Hu, Wenhan Luo +3
In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual info…
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment
Yunxin Li, Xinyu Chen, Baotian Hu +2
Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e.g., Q-former or MLP) focus o…
Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Yunxin Li, Zhenyu Liu, Baotian Hu +4
Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual i…
A Read-and-Select Framework for Zero-shot Entity Linking
Zhenran Xu, Yulin Chen, Baotian Hu +1
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability. Previous methods largely focus on the candidate retrieval…
Revisiting Sparse Retrieval for Few-shot Entity Linking
Yulin Chen, Zhenran Xu, Baotian Hu +1
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific doma…
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
Xinshuo Hu, Dongfang Li, Baotian Hu +3
Large language models (LLMs) have been widely used in various applications but are known to suffer from issues related to untruthfulness and toxicity. While parameter-efficient mod…
Generative Multimodal Entity Linking
Senbao Shi, Zhenran Xu, Baotian Hu +1
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base. Existing MEL methods mainly focus on design…
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues
Yunxin Li, Baotian Hu, Xinyu Chen +3
Conditional inference on joint textual and visual clues is a multi-modal reasoning task that textual clues provide prior permutation or external knowledge, which are complementary…
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text
Yunxin Li, Baotian Hu, Yuxin Ding +2
Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text. However, their performance drops drastically when confronted with lingui…
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction
Dongfang Li, Baotian Hu, Qingcai Chen +3
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification. However, previous works usually address one of tw…