TL;DR
A dynamic vision–language coupling framework that incorporates latent priors from visual semantics to guide prompt learning and employs a meta-network for adaptive visual prompt generation that advances multi-label emotion classification for ethnic artworks and cross-modal understanding.
A gated fusion mechanism incorporating a denoising feed-forward network is employed to effectively suppress intra-modal noise in audio and visual streams while achieving interactive multimodal affective fusion and masked graph learning is utilized to model conversational context among speakers, deeply capturing emotion...
The proposed Mamba-CrossMod is a novel multimodal feature fusion framework that introduces Mamba-ATT, an enhanced attention mechanism based on a selective state-space model for capturing long-range dependencies with theoretically linear complexity.
Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotional perception of a given image and explains the underlying rationale. Although related benchmarks ex...
A fine-tuned Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa) is used to offer a Transformer-based model for identifying multilabel emotional context in textual data to balance emotion categories and enhance the model's capacity for generalization.
Xiangju opera, a national intangible cultural heritage, encodes rich emotions and cultural symbolism through its musical language. However, computational frameworks that effectively capture the intrinsic coupling between affective expressions and cultural semantics remain scarce. This paper presents a deep learning-bas...
LG-GER is proposed, a language-guided distillation framework that uses a multimodal large language model (MLLM) to generate dense, spatially grounded evidence that achieves competitive or superior results compared to state-of-the-art methods that require detection and multi-stream processing at inference.
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