Multimedia Papers

ALT Music score files are more difficult to compare than their text-based equivalents, as their content is more complex and organised into hierarchical structures. This explains the lack of tools for processing music scores similar to the widely used old Unix diff command. We propose a new algorithm and tool for comparing digital music scores in various encodings, such as MusicXML or MEI, based on a graph-structured abstract representation called Tree Score Model (TSM). Given two scores, after conversion into TSM, our procedure calculates jointly: A list of differences, localised by time positions in the input scores; A time distance, cumulated duration of time-spans where scores differ; An edit distance, called surface distance, based on notational attributes not related to time. The two later measures are normalised and combined into a similarity distance between scores. Our procedure has been implemented in C++ and is available as a Python module. Our first experiments have shown a very

ALT Editing multi-camera concerts requires coordinated decisions about musical relevance, visual quality and cut timing. We present a modular framework that combines instrument activity from multitrack audio, visible instruments and image sharpness with beat-based timing and probabilistic camera selection. An interpretable scoring model links these components to the choice of views over complete shot intervals. Component tests and an exploratory expert study on a four-camera concert yield mixed assessments across the editing variants. The findings motivate extending local audiovisual relevance with contextual and sequence-level criteria. The architecture provides a basis for testing these extensions and studying how component behaviour affects the quality of complete edits.

ALT Multimodal misinformation detection methods often rely on different preprocessing pipelines and auxiliary resources, making experimental comparisons difficult to interpret. We present MMFact, a collection of shared resources and tools that simplifies this research workflow. It brings together 47 detector implementations and baselines, three multimodal datasets, and prepared materials ranging from semantic features to OCR, image descriptions, and external evidence. Compatible methods can use the same materials, reducing variation introduced by preprocessing and supporting fairer comparisons. A modular training and evaluation framework preserves method-specific designs while providing common experimental procedures. By treating prepared materials as a shared research resource, MMFact helps researchers focus on developing and evaluating detection methods.

ALT Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process. While multimodal large language models (MLLMs) excel at general video understanding, their ability to perform specialized editorial tasks remains unexplored. We introduce AdShot, a benchmark for evaluating MLLMs on shot selection for ad clipping. It contains 823 professionally edited ad pairs (30-sec sources and 15-sec edits) across 194 brands and 17 industries, annotated with shot-level boundaries and content features (action, emotion, information, imagery). We evaluate 13 state-of-the-art video-only MLLMs and 2 audio-visual MLLMs (7B-72B parameters) on shot selection accuracy (Precision, Recall, F1), duration constraints (15s), and content preservation, including an audio ablation. We find: (1) model architecture matters more than scale, with smaller models often matching or exceeding larger ones; performance is strongest for target ad

ALT This study develops an audio-visual emotion recognition system that integrates automatic utterance segmentation with multimodal fusion. Unlike conventional methods that rely on manually annotated utterance boundaries, the proposed system automatically generates candidate segments from continuous dialogue and extracts the corresponding audio and visual features for emotion classification. To address the challenges introduced by automatic segmentation, the proposed AGF-Net integrates Audio-Guided Fusion (AGF), Multimodal Negative Learning (MNL), and Predictive Dynamic Fusion (PDF) to improve cross-modal fusion, modality balance, and robustness to varying modality quality. Experimental results show that AGF-Net achieves 64.92% Accuracy and 65.47% F1-score under clean conditions, 62.13% Accuracy and 63.08% F1-score under moderate noise, and 58.19% Accuracy and 59.41% F1-score under higher noise. Overall, the proposed framework reduces reliance on manually annotated utterance boundaries and

ALT Reliable video perception over bandwidth-constrained wireless links requires transmitting information that the receiver cannot already recover from its evolving scene representation. This paper introduces WorldGT, a world-model-guided semantic packet transmission framework that prioritizes information according to its contribution to the receiver's perception. A shared feature-space world model predicts the receiver's scene state through affine motion compensation and learned residual displacement, while sender-side verification identifies reusable predictions. An acknowledgment-synchronized receiver twin estimates task-impact scores through two counterfactual evaluations, guiding packet selection and unequal error protection over LDPC-coded wireless channels. A compact 3-bit feature representation increases spatial coverage under stringent bandwidth constraints, while predictive fallback sustains perception during packet outages. Experiments on KITTI and VisDrone under Rayleigh fading

ALT Multimodal reasoning systems increasingly assume that longer chain-of-thought (CoT) uniformly improves downstream grounding. We show that this assumption fails in referring audio-visual segmentation (Ref-AVS): simple queries can be harmed by excessive reasoning, creating an "overthinking trap" associated with phrase and localization drift, while genuinely ambiguous queries still benefit from multi-step reasoning. To systematically study this, we construct counterfactual reasoning-budget labels under a five-fold out-of-fold evaluation protocol. Our results recast adaptive Ref-AVS reasoning as a pre-decisional representation problem: before the first generated token, the generation-onset state contains linearly decodable information predictive of whether additional reasoning will improve segmentation. A linear probe predicts binary reasoning need with 70.1% accuracy, substantially outperforming text-only features and uncertainty-based proxy signals. As a router, the onset-state controlle

ALT The optical electric network frequency, the faint mains-driven flicker imprinted on rolling-shutter video, is increasingly proposed for verifying where footage was captured and for flagging manipulated or generated video. This letter shows that reading the mains frequency from a single short clip is confounded by the camera frame rate: at the rates in common use, a harmonic of that rate lands in the same narrow band as the true flicker, so the recovered grid is set by the frame rate, not the supply, and the estimator returns a confident value instead of abstaining. The underlying aliasing is known; its forensic consequence is not, and is the contribution here. On the 804 clips with a measurable in-band line the flicker feature does not separate genuine from generated video (area under the curve 0.509), while the recovered grid tracks the frame rate exactly for the generators, and a controlled synthesis confirms the mechanism against ground truth for genuine grids. Single-clip grid, and

ALT Pretrained unimodal models offer a flexible route to multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains can weaken or reverse as inputs disappear, even when the anchor's own input is unchanged. Our diagnostics further reveal reduced recovery of modality-specific information after aggregation and state-dependent collaborative representations. We propose Lluna, which combines a direct private prediction path from adapted unimodal features with a common graph-based collaboration branch that provides a residual correction. Progressive Collaboration Consistency organizes richer-to-poorer guidance along single-modality removals, aligning normalized common representations while task supervision adapts predictions to the available evidence. Across eight datasets spanning affective computing, scene unde

ALT Continuous multimodal sentiment analysis estimates polarity and intensity from linguistic, acoustic, and visual cues that may disagree. Hard judgments compress sentiment alternatives, while learned fusion interfaces tie task adaptation to model representations. We introduce Shared Sentiment Evidence (SSE), an output-space interface with fixed view and sentiment-option semantics across frozen backbones. Four queries yield five-option distributions whose probabilities, entropy, and cross-view relations form a 28-dimensional vector for CatBoost regression. On CH-SIMS-v2 and CMU-MOSI, the Qwen-based interface improves on supervised joint-only readout. Matched Ridge and CatBoost controls assess the contributions of additional views, deterministic summaries, and nonlinear readout. With the same interface specification and fixed readout settings, separately fitted heads also improve on joint-only prediction for three additional frozen backbones. These results support learning continuous senti