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Found 5 papers, 1 papers with code
Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with Receptive-Field-Aware Attention Weighting
In this paper, we present a framework, Lantern, that can improve the performance of a certain vanilla model by prompting large language models with receptive-field-aware attention weighting.
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Additionally, these frameworks lack interpretability and user engagement during the training process, primarily due to the absence of human-centered design.
3DFacePolicy: Speech-Driven 3D Facial Animation with Diffusion Policy
This method generates variable and realistic human facial movements by predicting the 3D vertex trajectory on the 3D facial template with diffusion policy instead of facial generation for every frame.
MicroEmo: Time-Sensitive Multimodal Emotion Recognition with Micro-Expression Dynamics in Video Dialogues
Multimodal Large Language Models (MLLMs) have demonstrated remarkable multimodal emotion recognition capabilities, integrating multimodal cues from visual, acoustic, and linguistic contexts in the video to recognize human emotional states.
Panoptic-aware Image-to-Image Translation
The panoptic perception (i. e., foreground instances and background semantics of the image scene) is extracted to achieve alignment between object content codes of the input domain and panoptic-level style codes sampled from the target style space, then refined by a proposed feature masking module for sharping object boundaries.
