most citedMore Is Better: A MoE-Based Emotion Recognition Framework with Human Preference Alignment

1 citations · 1 across the 8 of their papers we have counts for

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8 papers

cs.CV2026

WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering

Yingjian Zhu, Xinming Wang, Kun Ding +3

Multi-modal Retrieval-Augmented Generation (RAG) has emerged as a highly effective paradigm for Knowledge-Based Visual Question Answering (KB-VQA). Despite recent advancements, pre…

cs.CV2026

SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation

Yingjian Zhu, Ying Wang, Yuyang Hong +5

Recently, an audio-visual instance segmentation (AVIS) task has been introduced, aiming to identify, segment and track individual sounding instances in videos. However, prevailing…

cs.CV2025

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM

Lin Chen, Yingjian Zhu, Qi Yang +3

Open-vocabulary semantic segmentation (OVSS) aims to segment and recognize objects universally. Trained on extensive high-quality segmentation data, the segment anything model (SAM…

cs.CL2025

MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models

Xinming Wang, Jian Xu, Bin Yu +9

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation i…

cs.CV20251 cited

More Is Better: A MoE-Based Emotion Recognition Framework with Human Preference Alignment

Jun Xie, Yingjian Zhu, Feng Chen +9

In this paper, we present our solution for the semi-supervised learning track (MER-SEMI) in MER2025. We propose a comprehensive framework, grounded in the principle that "more is b…

cs.CV2025

Multimodal Video Emotion Recognition with Reliable Reasoning Priors

Zhepeng Wang, Yingjian Zhu, Guanghao Dong +4

This study investigates the integration of trustworthy prior reasoning knowledge from MLLMs into multimodal emotion recognition. We employ Gemini to generate fine-grained, modality…