HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition
Authors: Aleksei Bakin, Andrey V. Savchenko
Organizations: Central University, Moscow, Russia · Sber AI Lab, Moscow, Russia · HSE University, Laboratory of Algorithms and Technologies for Network Analysis, Nizhny Novgorod, Russia
Abstract
This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on s-Aff-Wild2 dataset, we use frozen lightweight facial extractors, MT-EmotiDDAMFN and MT-EmotiEffNet-B0, with separate heads and systematic post-processing: temporal Gaussian smoothing, per-class expression bias, AffectNet blending, per-AU threshold tuning, and weighted backbone fusion. On the official validation set, our ensemble significantly exceeds the performance of the ConvNeXt baseline. For ambivalence/hesitancy video recognition on the expanded BAH dataset, we extend the audiovisual pipeline to video-level Macro F1 by late fusion of face, HuBERT audio, and RoBERTa text classifiers, temporal aggregation, and a global-text gate. Frame-level Weighted F1 on validation set rises from 0.74 in ABAW-8 to 0.79, while the best public-test video-level Macro F1 reaches 0.73. In both tasks, competitive performance is achieved without fine-tuning heavy backbones. These results indicate that systematic prediction calibration and lightweight multimodal fusion can rival substantially heavier end-to-end approaches while offering improved efficiency and deployment flexibility.
Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles. We present a single text-centered multimodal approach for video-level ambivalence and hesitancy recognition for the 11th Affective & Behavior Analysis in-the-Wild (ABAW) Challenge. The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered multimodal fusion model. Text Residual Fusion treats text as the anchor modality and applies gated residual adjustments based on the other modalities. Experiments on the Behavioural Ambivalence/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality. The Text Residual Fusion model achieves an average Macro F1-score (MF1) of 75.14% across the Development and Public Test subsets. On the Private Test subset, it reaches an MF1 of 78.24%, outperforming the text model by 4.03%. These results demonstrate that complementary multimodal information can improve recognition performance without requiring a large model ensemble.
Elena Ryumina, Maxim Markitantov, Alexandr Axyonov +4
We present a multimodal framework for Ambivalence/Hesitancy (A/H) recognition in video, developed for the ABAW11 challenge at ECCV 2026. The proposed approach fuses textual, acoustic, and visual modalities extracted from the BAH dataset using three pretrained encoders: F2LLM-v2-0.6B for transcripts (1024-d), WavLM-Large for audio (1024-d), and VideoMAE V2 for facial video (768-d). We first establish comprehensive unimodal baselines using classical classifiers (MLP, Random Forest, GBDT), each optimized via Optuna, and obtain a best unimodal Macro F1 of \textbf{0.6659} on the test set using text features alone -- substantially outperforming the zero-shot Video-LLaVA baseline (Macro F1: 0.2827). Building on these baselines, we propose a multimodal fusion architecture that combines bidirectional cross-attention across all three modalities with a Gated Multimodal Unit (GMU), with both architectural and optimization hyperparameters selected through a 50-trial Optuna search. This model achieves a Macro F1 of \textbf{0.7394} on the validation set, a relative improvement of 11.0% over the best unimodal baseline, confirming that explicit cross-modal interaction captures complementary cues that no single modality provides in isolation. Final predictions on the official, unlabeled private test set are generated using this model and submitted according to the challenge protocol. Code is publicly available at https://github.com/yassineouzar/IUSD_AH/
The 11th Affective Behavior Analysis in-the-wild (ABAW11) Multi-Task Learning Challenge requires a unified system to predict valence-arousal, categorical expressions, and facial action units from the official s-Aff-Wild2 images. Although these tasks are naturally related through facial behavior, our validation experiments show that they benefit from different visual features, temporal processing strategies, fusion mechanisms, and calibration procedures. In this paper, we study task-adaptive feature fusion for ABAW11 multi-task affective behavior analysis. We first adapt two pretrained visual backbones, DINOv2 ViT-L and DINOv3 ConvNeXt-base, on an external expression-oriented facial image set and then freeze them to extract complementary frame-level features from the official ABAW11 data. On top of these frozen features, we systematically compare frame-level prediction heads, temporal convolutional heads, post-hoc temporal smoothing, LightGBM models, feature concatenation, gated fusion, residual fusion, late logit fusion, threshold calibration, and shared MTL structures. The final system selects task-specific fusion and prediction strategies rather than forcing all tasks to share a single architecture. On the ABAW11 validation set, the selected system achieves an EXPR macro-F1 of 0.4222, an AU macro-F1 of 0.5402, and a mean VA CCC of 0.6717, resulting in an overall validation score of 1.6341. The results suggest that task-adaptive fusion of frozen visual features is a simple and effective strategy for ABAW-style multi-task affective behavior analysis.