We present an audio-text system for the Ambivalence/Hesitancy Video Recognition Challenge of the 11th ABAW Competition. The method excludes visual frames and represents each video as overlapping 5-second windows aligned with transcript timestamps.
arXiv:2607.13345v2 Announce Type: replace Abstract: We present a frame-independent audio-text system for the 3rd Ambivalence/Hesitancy Video Recognition Challenge at the 11th Affective & Behavior Ana...
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
The paper introduces the Modality Discrepancy Transformer (MDT), a model designed to detect ambivalence and hesitancy in clinical videos by capturing cross‑modal disagreement across facial, vocal, and linguistic signals. MDT expands a 6‑token representation to 9 tokens that include modality embeddings, absolute‑difference features, and Hadamard‑product discrepancy features, which are processed through Transformer self‑attention with FiLM‑based text conditioning and LoRA fine‑tuning. On the BAH dataset from the 3rd ABAW Challenge, MDT achieves a Macro F1 score of 0.7408 on the labelled test split and 0.7368 on the private leaderboard, surpassing the strongest baseline by over 10 points while training in under 20 minutes on a single GPU.
arXiv:2607. 14702v1 Announce Type: cross Abstract: 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.
The paper introduces a multimodal emotion recognition framework that combines audio and visual feature extraction with an attention-based fusion strategy. Audio features include Wav2Vec2 embeddings, MFCCs, and statistical acoustic descriptors, fused via a BiLSTM, while video features are extracted using a ResNet50-BiLSTM architecture. A multi-head attention mechanism fuses these modalities, and experiments on MELD and IEMOCAP show significant accuracy and robustness gains, especially in unbalanced data settings.
The paper introduces RAFM-SER++, a lightweight multimodal speech emotion recognition framework designed for real‑time surveillance systems. It replaces heavy bidirectional cross‑modal transformers with an asymmetric Residual Attention Fusion Mechanism that injects affective speech cues into text representations via a one‑directional residual attention pathway. Experiments on IEMOCAP and ESD show RAFM‑SER++ outperforms the HuBERT‑Base baseline and MemoCMT, reducing trainable parameters by over 60%, achieving 79.60 it/s inference speed, and reaching BACC scores of 81.10% on IEMOCAP and 95.39% on ESD.
arXiv:2607. 16635v1 Announce Type: cross Abstract: We present TellTale, a text-only approach to ambivalence/hesitancy (A/H) recognition in interview videos, evaluated on the BAH dataset as part of the 3rd A/H Video Recognition Challenge (11th ABAW Workshop, ECCV 2026).
arXiv:2512.07571v3 Announce Type: replace Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...
arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.
arXiv:2607. 03825v1 Announce Type: cross Abstract: Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio.
arXiv:2608. 19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance.
LEAP is a framework for long audio‑video question answering that avoids encoding entire recordings by dividing them into fixed‑duration blocks. It performs a lightweight localization pass on each block to score short candidate windows, then pools the highest‑ranked windows for a single bounded answer pass, keeping the answer input and peak context independent of recording length. The method trains both a localization LoRA and an answer LoRA, supports causal streaming queries, and achieves significant performance gains over baseline models on multiple AVQA benchmarks.