arXiv subjects

Ye Lou

Publications and source records attributed to Ye Lou.

3 recordsLinked to original sources

arXiv2026

State-Anchored Complete-View Distillation for Robust Conversational Multimodal Emotion Recognition

Conversational multimodal emotion recognition (MER) requires reliable prediction when language, acoustic, or visual observations are missing or unreliable. Many missing-modality methods reconstruct absent inputs, yet such recovery can be non-unique in dialogue context, and nonverbal cues may conflict with the target utterance. To this end, we propose CoRe-KD (Complete-view Reference-guided Knowledge Distillation), a state-anchored, conflict-regularized complete-view distillation framework for robust conversational MER. A complete-view teacher provides structured references, including prediction-level references, fused states, and modality-specific states. Complete-view State Anchoring (CSA) aligns incomplete-view student predictions and states with these references, while Nonverbal Conflict Exposure (NCE) trains on target-preserving nonverbal conflict views to reduce donor-label bias. Experiments on IEMOCAP and MELD, with CMU-MOSEI as a supplementary utterance-level check, show consistent gains under fixed- and random-missing protocols. Comprehensive ablation studies and further analyses support the role of CSA and the complementary effect of NCE.

cs.MM↗

arXiv2026

Beyond Isolated Utterances: Cue-Guided Interaction for Context-Dependent Conversational Multimodal Understanding

Conversational multimodal understanding aims to infer the meaning or label of the current utterance from its preceding dialogue context together with textual, acoustic, and visual signals. Existing methods mainly strengthen contextual modeling through enhanced encoding, fusion, or propagation, but rarely abstract the context-utterance dependency into an explicit cue and incorporate it into later multimodal reasoning. To address this issue, we propose CUCI-Net for conversational multimodal understanding. CUCI-Net fully preserves the structural distinction between context and utterance during encoding, effectively abstracts their dependency into an interpretation cue by combining local modality evidence with global contextual evidence, and seamlessly integrates the resulting cue into the final multimodal interaction stage for context-conditioned prediction. Extensive experiments on mainstream benchmark datasets fully demonstrate the effectiveness of the proposed method.

cs.MM↗

arXiv2026

MAUGen: A Unified Diffusion Approach for Multi-Identity Facial Expression and AU Label Generation

The lack of large-scale, demographically diverse face images with precise Action Unit (AU) occurrence and intensity annotations has long been recognized as a fundamental bottleneck in developing generalizable AU recognition systems. In this paper, we propose MAUGen, a diffusion-based multi-modal framework that jointly generates a large collection of photorealistic facial expressions and anatomically consistent AU labels, including both occurrence and intensity, conditioned on a single descriptive text prompt. Our MAUGen involves two key modules: (1) a Multi-modal Representation Learning (MRL) module that captures the relationships among the paired textual description, facial identity, expression image, and AU activations within a unified latent space; and (2) a Diffusion-based Image label Generator (DIG) that decodes the joint representation into aligned facial image-label pairs across diverse identities. Under this framework, we introduce Multi-Identity Facial Action (MIFA), a large-scale multimodal synthetic dataset featuring comprehensive AU annotations and identity variations. Extensive experiments demonstrate that MAUGen outperforms existing methods in synthesizing photorealistic, demographically diverse facial images along with semantically aligned AU labels.

cs.CV↗