Multimodal Genre Classification

Updated 11 November 2025

Multimodal Genre Classification is an approach that categorizes media artifacts using fused audio, text, visual, and metadata signals for robust genre analysis.
It employs diverse fusion architectures such as early/late fusion and cross-modal attention to overcome unimodal limitations.
Empirical results across TV, music, movies, books, and games demonstrate significant accuracy gains and improved semantic clustering.

Multimodal genre classification is the automatic categorization of media artifacts—such as films, TV broadcasts, books, comic narratives, games, or musical works—into genre categories, leveraging multiple heterogeneous modalities (text, audio, image, metadata). This approach addresses both the structural complementarity of modalities and the ambiguity or insufficiency of unimodal features. Systems are typically evaluated on large-scale, multi-class or multi-label datasets, requiring robust architectures for feature extraction, fusion, and learning under severe class imbalance and inter-class semantic overlap.

1. Problem Definition and Motivation

Multimodal genre classification aims to assign genre labels to complex media artifacts using information drawn from several modalities. These may include:

Audio (speech, music, sound effects),
Textual content (subtitles, plot summary, dialogue, OCR-extracted text, lyrics),
Visual cues (cover/poster art, frames, scenes, video object detections, panel layouts),
Structured metadata (broadcast time/channel, director/cast, knowledge graphs).

The primary motivation is that genre is an inherently multimodal construct: acoustic patterns, linguistic tropes, visual style, and even context-specific metadata each contribute genre-discriminant cues. Unimodal approaches suffer from reduced accuracy due to missing or noisy signals, and genre boundaries are routinely crossed, e.g., through hybrid works or ambiguous labeling.

Applications include broadcast meta-data annotation (Doulaty et al., 2016), media retrieval and recommendation (Ru et al., 2023, Sulun et al., 11 Oct 2024), large-scale archiving, and fine-grained semantic clustering (Fish et al., 2020). Incorporating complementary modalities has been demonstrated to produce significant absolute gains in both top-1 accuracy and macro-averaged F1/mAP across domains such as TV (Doulaty et al., 2016), music (Ru et al., 2023, Agrawal et al., 2020, Schindler, 2020), movies (Sulun et al., 11 Oct 2024, Fish et al., 2020, Li et al., 2023, Mangolin et al., 2020, Vishwakarma et al., 2021), books (Kundu et al., 2020), video games (Jiang et al., 2020), and comics (Chen et al., 2023).

2. Modalities, Feature Extraction, and Representations

A. Audio

Key audio features include 13D PLP (Perceptual Linear Prediction) (Doulaty et al., 2016), MFCCs (Mangolin et al., 2020, Schindler, 2020), spectrogram-based CNN embeddings (Agrawal et al., 2020, Sulun et al., 11 Oct 2024), and high-level event/music classifiers (e.g., AudioTag, MusicNet) (Sulun et al., 11 Oct 2024). Temporal windowing may range from 10 ms (speech features) up to seconds-long chunks for music or audio event annotation.

B. Text

Textual signals may be derived from subtitles, plot synopses, OCR’d titles, speech recognition outputs, or lyrics. Preprocessing typically includes tokenization, sometimes TF–IDF or bag-of-words; transformer-based representations (BERT, DistilBERT, CLIP text encoder, USE) are now prevalent (Ru et al., 2023, Kundu et al., 2020, Jiang et al., 2020, Nareti et al., 12 Oct 2024). For dialog or lyrics, hierarchical encoders (e.g., HAN with word- and sentence-level attention) are also used (Agrawal et al., 2020).

C. Visual

Visual features span:

Low-level: color histograms, LBP (Mangolin et al., 2020, Schindler, 2020), global image statistics.
Mid-level: panel layout and transition stats (for comics) (Chen et al., 2023).
High-level: pretrained CNNs—e.g., ResNet-50 for book/game/poster covers (Kundu et al., 2020, Jiang et al., 2020), CLIP-ViT for movie frames/posters (Sulun et al., 11 Oct 2024, Nareti et al., 12 Oct 2024), AlexNet for music video frames (Schindler, 2020).
Object and scene detectors for video (Fish et al., 2020, Sulun et al., 11 Oct 2024).

D. Metadata and Structured Knowledge

Time-bin/channel (Doulaty et al., 2016), cast/director/title-entity graphs (Li et al., 2023), plot summaries, keywords or knowledge graphs embedding entity-type and group relations, often serve as crucial auxiliary modalities.

3. Fusion Architectures

A. Early/Late Fusion

Early fusion: Feature vectors from each modality are concatenated before classification (Kundu et al., 2020, Jiang et al., 2020, Mangolin et al., 2020).
Late fusion: Separate classifiers per modality, outputs fused (averaged, product, or learned weights) at decision level (Wang et al., 2017, Mangolin et al., 2020).

Cross-attention modules: Explicitly attending between text and image (e.g., MCAM+SMSAM on movie posters (Nareti et al., 12 Oct 2024)), symmetric cross-modal attention blocks between audio/lyrics (Ru et al., 2023).
Self-attention over modalities: Transformer fusion across all video, audio, and text streams enables temporal and cross-modal dependencies to be captured without coarse pooling (Sulun et al., 11 Oct 2024).
Collaborative gating: Modality-wise attention (gating) learned via self-supervised or graph-centric pseudo-labels stabilizes fusion weights, as in genre classification over multiple sources (CLIP, KG) (Li et al., 2023, Fish et al., 2020).

C. Graph-Based and Knowledge-Enhanced Fusion

Genre co-occurrence graphs/GCNs: Modeling inter-genre correlation via GCNs over co-occurrence and semantic similarity matrices (Music4All (Ru et al., 2023)).
Domain knowledge graphs: Metadata is structured into multi-entity KGs (director, cast, genre, title) and embedded; graph context is fused with visual/textual encodings using attention mechanisms and anchored contrastive learning (Li et al., 2023).

4. Learning Algorithms and Loss Functions

A. Classifiers

SVMs are common in pipelines with topic-model features as input (BBC TV (Doulaty et al., 2016)), as is linear/logistic regression as output layer in transformer models (Sulun et al., 11 Oct 2024).
MLPs and CNN/LSTM heads atop fused features are standard (Jiang et al., 2020, Kundu et al., 2020, Agrawal et al., 2020, Mangolin et al., 2020).
Mixture-of-Experts architectures (MoE) leverage learned gating for ensemble of logistic regression "experts" per class or modality (Wang et al., 2017).

B. Losses

(Weighted) Binary cross-entropy (BCE) for multi-label genre assignments, with positive-class reweighting to address severe genre imbalance (Sulun et al., 11 Oct 2024, Li et al., 2023, Ru et al., 2023).
Asymmetric loss (ASL): Combats genre frequency skew by applying class-dependent powers and a margin on negatives (Nareti et al., 12 Oct 2024).
Contrastive and correlation loss: For aligning modality-specific embeddings (contrastive loss across audio–text or knowledge–fusion space) (Ru et al., 2023, Li et al., 2023, Fish et al., 2020); Deep CCA (DCCA) for enforcing correlated representation (Kundu et al., 2020).

5. Domain-Specific Implementations

Acoustic and text streams are discretized as "documents," LDA-segment topic mixtures are fused with time/channel metadata, and linear SVMs yield 98.6% accuracy on 8-way BBC genre ID.

Contrastive audio–lyric alignment, cross-modal attention, and GCN genre modeling attained F-measure 0.534 (Music4All). Fused networks using CNNs for audio, HAN/GRUs for lyrics achieved 76.2% accuracy (FMA-medium).
Audio-visual music video classification achieves >16 pp improvement by fusing CNN-concept features with statistical audio, confirming that visual stereotypes (e.g., "cowboy_hat" for Country) are genre-discriminant (Schindler, 2020).

Multi-stream transformer fusion outperforms baselines on MovieNet, achieving mAP=66.02 (+35% over prior) via whole-trailer attention (Sulun et al., 11 Oct 2024).
Knowledge graph fusion through attention and contrastive learning delivers micro-F1=0.849 (MM-IMDb), ablates to macro=0.645 on prior graph baselines (Li et al., 2023).
Fine-grained clustering via contrastive fine-tuning on multi-expert trailer embeddings exposes semantic groupings beyond standard coarse labels (Fish et al., 2020).
Multi-modal late fusion (synopsis LSTM + frame CNN) on 18 genres reaches F1=0.628, showing that strong textual and visual models are complementary (Mangolin et al., 2020).
Cross-attention-based fusion (CLIP vision/text) of poster images/text achieves macro-F1=68.23%, outperforming CentralNet/GMU by >11 pp (Nareti et al., 12 Oct 2024).
Frame-based situation extraction, dialogue/metadata feature fusion on movie trailers yields AU(PRC)=0.92/0.82 (EMTD/LMTD-9) (Vishwakarma et al., 2021).
Mixture-of-Experts ensemble combining title, keyword, audio, and vision models achieves 86.7% GAP on YouTube-8M-Text (Wang et al., 2017).

Cover image LSTM+USE text fusion (book genre) boosts top-1 accuracy to 56.1% (from 29.6% or 52.6% on image-/text-only branches); per-genre confusion reflects semantic and stylistic overlaps (Kundu et al., 2020).
For video games, late-fused ResNet-50 images and USE text encoders attain 49.9% top-1, 79.9% top-3 accuracy on 15-way task (Jiang et al., 2020).

Multimodal genre classification exploiting panel transition frequencies, CNN visual stats, and text/box densities lifts macro-F1 to 0.64 (+0.09 over visual-only). Transition-specific features (e.g., action-to-action) explain genre separability (battle/sport, fantasy/romance).

6. Empirical Results and Ablations

All studies report that fusing modalities yields statistically and practically significant gains over unimodal baselines.

Domain	Dataset / Task	Unimodal Best	Multimodal Best	Absolute Gain
TV broadcast	BBC, 8 genres (Doulaty et al., 2016)	Text LDA 96.2%	Audio+Text+Meta 98.6%	+2.4%
Music	Music4All (Ru et al., 2023)	Audio 0.419 F1	A+L+Attn+GCN 0.534 F1	+11.5 pp
Movies	MovieNet (Sulun et al., 11 Oct 2024)	Late fusion 46.88 mAP	Multi-Tfr 66.02 mAP	+19.14 mAP
Video games	50k, 15 genres (Jiang et al., 2020)	USE 47.7%	Image+Text 49.9%	+2.2%
Books	BookCover30, 30 genres (Kundu et al., 2020)	USE 52.6%	Image+Text 56.1%	+3.5%

Ablation consistently reveals:

Late fusion and cross-attention outperform naive concatenation.
Attention and contrastive learning modules mitigate the noise and imbalance of real-world genre-labeled datasets.
Visual-only models underperform for nuanced or textually defined genres; text models dominate but benefit measurably from visual input.

7. Challenges, Analysis, and Future Directions

Several key challenges are reported:

Label noise and inter-/intra-genre ambiguity, especially with single-label assignments ("Travel"/"Cooking"/"History" mixes in books (Kundu et al., 2020)).
Subjectivity in annotation and genre boundaries (e.g., manga/graphic novels (Chen et al., 2023)).
Imbalanced label distributions and rare classes (e.g., "Film-Noir" in MM-IMDb 2.0 (Li et al., 2023)), mitigated by advanced loss functions (ASL, class-weighted BCE).
Inconsistent or sparse modalities (movie posters often have little text (Nareti et al., 12 Oct 2024); ASR errors degrade performance (Doulaty et al., 2016)).
Computational cost from the need to process large numbers of dense or long temporal sequences across modalities (Sulun et al., 11 Oct 2024, Mangolin et al., 2020).

Future directions include:

More advanced cross-modal and knowledge-based attention/fusion mechanisms (e.g., Transformer-based, cross-modal retrieval, knowledge graph expansion) (Li et al., 2023, Sulun et al., 11 Oct 2024, Nareti et al., 12 Oct 2024).
Integration of additional sources (e.g., full film content, external metadata, synopses, or character graphs).
Unsupervised or semi-supervised clustering for discovering sub-genres or emergent categories beyond coarse labels (Fish et al., 2020).
Domain adaptation, data augmentation (esp. for rare genres), and graph-driven label propagation for improved generalization in tail classes.

A plausible implication is that continued improvements in genre classification performance will increasingly depend on nuanced modeling of inter-genre semantic relationships, multimodal input alignment, and informed exploitation of metadata and structured knowledge. Attention-based cross-modal architectures and graph-augmented learning are expected to remain areas of high activity and rapid progress.