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20242026
most citedIISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT

38 citations · 48 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.IR2026

Optimizing VLP-aligned Multimodal Intent Representation with Correct Visual Instantiation for Zero-Shot Composed Image Retrieval

Xuri Ge, Chunhao Wang, Junchen Fu +7

ZS-CIR aims to retrieve a target image from a reference image and a modification text without paired supervision, typically by encoding composed queries as text-dominant representa…

cs.IR2026

Tracing Query Expansion Effects through Sparse Autoencoder Features

Fangan Dong, Weiran Shi, Zhiwei Xu +5

Query expansion (QE) is a critical technique in information retrieval that enriches underspecified queries with additional textual context. However, its effect is often unreliable…

cs.IR2026

Stream-aware Side Adaptation for Large Pre-trained Multimodal Embedding Models in Sequential Recommendation

Junchen Fu, Kaiwen Zheng, Ioannis Arapakis +4

Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item re…

cs.CV2026

MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

Xuri Ge, Chunhao Wang, Xindi Wang +3

Composed Image Retrieval (CIR) aims to retrieve target images based on a reference image and modified texts. However, existing methods often struggle to extract the correct semanti…

cs.CL2026

Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering

Fangan Dong, Zuming Yan, Xuri Ge +7

Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires post-training or computationall…