Compound Expression Recognition via Large Vision-Language Models

Compound Expression Recognition (CER) is crucial for understanding human emotions and improving human-computer interaction. However, CER faces challenges due to the complexity of facial expressions and the difficulty of capturing subtle emotional cues. To address these issues, we propose a novel approach leveraging Large Vision-Language Models (LVLMs). Our method employs a two-stage fine-tuning process: first, pre-trained LVLMs are fine-tuned on basic facial expressions to establish foundational patterns; second, the model is further optimized on a compound-expression dataset to refine visual-language feature interactions. Our approach achieves advanced accuracy on the RAF-DB dataset and demonstrates strong zero-shot generalization on the C-EXPR-DB dataset, showcasing its potential for real-world applications in emotion analysis and human-computer interaction.

Solution for 8th Competition on Affective & Behavior Analysis in-the-wild

In this report, we present our solution for the Action Unit (AU) Detection Challenge, in 8th Competition on Affective Behavior Analysis in-the-wild. In order to achieve robust and accurate classification of facial action unit in the wild environment, we introduce an innovative method that leverages audio-visual multimodal data. Our method employs ConvNeXt as the image encoder and uses Whisper to extract Mel spectrogram features. For these features, we utilize a Transformer encoder-based feature fusion module to integrate the affective information embedded in audio and image features. This ensures the provision of rich high-dimensional feature representations for the subsequent multilayer perceptron (MLP) trained on the Aff-Wild2 dataset, enhancing the accuracy of AU detection.

Dual-Stage Cross-Modal Network with Dynamic Feature Fusion for Emotional Mimicry Intensity Estimation

Emotional Mimicry Intensity (EMI) estimation serves as a critical technology for understanding human social behavior and enhancing human-computer interaction experiences, where the core challenge lies in dynamic correlation modeling and robust fusion of multimodal temporal signals. To address the limitations of existing methods in insufficient exploitation of modal synergistic effects, noise sensitivity, and limited fine-grained alignment capabilities, this paper proposes a dual-stage cross-modal alignment framework. First, we construct vision-text and audio-text contrastive learning networks based on an improved CLIP architecture, achieving preliminary alignment in the feature space through modality-decoupled pre-training. Subsequently, we design a temporal-aware dynamic fusion module that combines Temporal Convolutional Networks (TCN) and gated bidirectional LSTM to respectively capture the macro-evolution patterns of facial expressions and local dynamics of acoustic features. Innovatively, we introduce a quality-guided modality fusion strategy that enables modality compensation under occlusion and noisy scenarios through differentiable weight allocation. Experimental results on the Hume-Vidmimic2 dataset demonstrate that our method achieves an average Pearson correlation coefficient of 0.35 across six emotion dimensions, outperforming the best baseline by 40\%. Ablation studies further validate the effectiveness of the dual-stage training strategy and dynamic fusion mechanism, providing a novel technical pathway for fine-grained emotion analysis in open environments.

Space-Time-Modulated Wideband Radiation-Type Programmable Metasurface for Low Sidelobe Beamforming

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually possess a relatively high profile as a result of the external feed source. Besides, it is difficult to conduct multibit phase shift in metasurfaces, when comparing with conventional phased arrays. Here, we propose a strategy of space-time modulated wideband radiation-type programmable metasurface for low side-lobe beamforming. The wideband programmable metasurface avoids the space-feed external source required by its traditional counterpart, thus achieving a significant reduction of profile through integration of a highefficiency microwave-fed excitation network and metasurface. Furthermore, through introducing space-time-modulated strategy, the high-accuracy amplitude-phase weight algorithm can also be synchronously carried out on the first harmonic component for low side-lobe beam-scanning. Most importantly, adaptive beamforming and generation of interference null can further be created after analyzing the harmonic component characteristics of received signals.