Hierarchical Video Captioning

Updated 4 July 2025

Hierarchical video captioning is a computational paradigm that produces layered, coherent descriptions by modeling complex temporal and semantic structures.
It employs architectures like h-RNN and HRNE that use stacked RNNs and adaptive attention to capture fine- and coarse-grained video details.
This approach enhances video summarization, precise indexing, and assistive technologies by providing detailed, multi-level natural language outputs.

Hierarchical video captioning refers to the set of computational paradigms and model architectures aimed at generating coherent, multi-granular natural language descriptions for videos that exhibit complex temporal, logical, and semantic structure. Unlike traditional approaches that produce a single sentence for a short video clip, hierarchical video captioning systems generate multi-level descriptions—including sentences, paragraphs, actions, or stepwise instructions—leveraging explicit video and language hierarchy. This approach is critical for processing real-world videos, which frequently contain multiple events, actions, or scenes and require descriptions at varying temporal and conceptual scales.

1. Hierarchical Model Architectures

A wide range of neural architectures have been developed to capture the temporal and compositional hierarchy present in videos. Early frameworks, such as the Hierarchical Recurrent Neural Network (h-RNN) (1510.07712), explicitly model structure across two layers:

Sentence generator (lower level): Produces a sequence of words describing a short video segment, using RNNs (e.g., GRU) with attention mechanisms.
Paragraph generator (upper level): Operates over the sequence of generated sentence embeddings, using a second RNN to encode inter-sentence (inter-event) dependencies and initialize subsequent sentence generations.

Later architectures generalize or enrich this design. The Hierarchical Recurrent Neural Encoder (HRNE) (1511.03476) introduces multi-layer LSTM stacks to process video features in temporal chunks, capturing both fine- and coarse-grained sequential dependencies. Hierarchical LSTM approaches with adjusted or adaptive attention (1706.01231, 1812.11004) further disentangle low-level visual processing from high-level linguistic context. These decoders often apply gating mechanisms to balance reliance on visual input and language context (e.g., via $\beta_t$ gating scalars).

Recent models incorporate multimodal and modular hierarchies: the Hierarchical Modular Network (HMN) (2111.12476) fuses object-level, action-level, and sentence-level representations, each explicitly supervised with linguistic targets; other frameworks employ memory-augmented hierarchies (2002.11886) or graph-based multi-modal hierarchies (2308.06685).

Model	Hierarchical Structure	Key Features
h-RNN	Sentence/paragraph RNNs	Inter-sentence state propagation, attention (spat/temporal)
HRNE	Chunked multi-layer LSTM encoder	Long-range temporal structure, multi-scale modeling
hLSTM/hLSTMat	Stacked LSTMs, adaptive attention	Context-dependent word grounding
HRL	Manager/worker modules (RL-based)	Subgoal setting, clause/phrase segmentation
HMN	Entity/predicate/sentence modularity	Explicit supervision at multiple linguistic levels
Dual-graphs+Gate	Multi-graph feature hierarchy + fusion	Frame-frame/object reasoning, adaptive fusion
Video ReCap	Recursive video–LLM	Multi-level captioning up to hours of video

2. Temporal and Semantic Hierarchy Modeling

Hierarchical video captioning systems are designed to capture structure at multiple temporal scales:

Low-level (atomic) events: Typically modeled by word-level RNNs or short-span encoders, responsible for describing immediate objects and actions.
Sentence/segment-level: Models aggregate or propagate context across contiguous actions, utilizing paragraph-level RNNs or segmental encoders.
High-level summaries: Some architectures (e.g., Video ReCap (2402.13250)) recursively compose short-range descriptions into progressively coarser summaries, up to full video abstraction spanning tens of minutes.

Attention mechanisms play a pivotal role in selecting salient content at each hierarchy:

Temporal attention enables focus on key frames or intervals for each word/sentence (1510.07712, 1812.11004).
Spatial attention allows the decoder to attend to salient regions or object proposals within frames.
Global-to-local attention (e.g., Global2Local (2203.06663)) and stacking attention (2009.07335) implement multistage selection: first over clips/accounts for global context, then over localized detail.

Semantic hierarchy is further exploited by supervising modules with object, predicate, or sentence-level linguistic targets (2111.12476) and by explicit architectural separation of context pathways for compositional reasoning.

3. Learning and Supervision Strategies

Supervised training typically utilizes sequence losses (cross-entropy, negative log-likelihood) at the word or sentence level. Hierarchical architectures introduce several innovations:

Multi-level supervision: HMN (2111.12476) aligns visual representations with corresponding entities, predicates, and global sentence embeddings using auxiliary losses and assignments (e.g., Hungarian matching for object-entity alignment).
Curriculum learning: Video ReCap (2402.13250) trains models to caption first at atomic (clip) level, then segment, and finally at the summary level, mirroring human perception and overcoming data imbalance.
Information-focused learning: The Information Loss strategy (1901.00097) up-weights losses for rare, video-specific words based on both information relevance and content to counter dataset bias and improve specificity.
Reinforcement learning (RL): Hierarchical RL frameworks (1711.11135, 2212.10690) optimize for sequence-level metrics (e.g., CIDEr, METEOR) across modular "manager" and "worker" policies, employing policy gradient and hierarchical reward structures. The BMHRL Transformer (2212.10690) fuses audio and video inputs, integrating reward-guided divergence that makes the model robust to token permutations.

4. Evaluation and Empirical Results

Benchmarks for hierarchical video captioning span both automatic and human metrics:

Automatic metrics: BLEU@4, METEOR, ROUGE-L, CIDEr are standard; CIDEr, in particular, strongly rewards distinctive, video-specific n-grams.
Human evaluations: Assess narrative coherence and semantic fidelity, especially for multi-sentence or paragraph-level output.
Novel metrics: Semantic Sensibility (SS) scoring (2009.07335) evaluates grammar, object/action recall and precision, jointly considering informativeness and correctness.

Table: Exemplary results (h-RNN (1510.07712)):

Method	YouTubeClips (B@4)	TACoS-MultiLevel (B@4)
LSTM-E	0.453	-
LRCN	-	0.292
h-RNN	0.499	0.305

Recent approaches set new performance records on widely used benchmarks like MSVD, MSR-VTT, and domain-specific datasets such as TACoS-MultiLevel, Charades Captions, and Ego4D-HCap. Hierarchical and modular architectures consistently outperform flat sequence models in both sentence- and paragraph-level tasks.

5. Applications and Practical Impact

Hierarchical video captioning has become crucial for:

Video summarization: Structured paragraph or multi-sentence output enables natural summarization for long, untrimmed videos.
Assistive technology: Rich, coherent multi-sentence captions improve accessibility for visually impaired users.
Instructional and egocentric video understanding: Step-level and action/goal-level descriptions facilitate skill acquisition and analytics in complex instructional or first-person video.
Fine-grained indexing and retrieval: Hierarchy-aware representations allow indexing at action, step, or event granularity.
Video-based Question Answering (VideoQA): Hierarchical captions provide multi-level context, boosting VQA accuracy on long videos (2402.13250).

Incorporation of object and predicate alignment (2111.12476), joint audio-visual modeling (2212.10690), and boundary-aware segmentation (1611.09312, 1807.03658) enable systems to process unconstrained, real-world video data with complex event composition.

6. Ongoing Directions and Research Challenges

Despite empirical progress, several open challenges remain:

Scalability to very long videos: Recursive design (Video ReCap (2402.13250)), curriculum learning, and hierarchical compression address the feasibility of modeling hour-long content, but coherent abstraction at higher levels remains demanding.
Automated segmentation and step-wise summarization: Joint models that combine retrieval, segmentation, and per-step captioning (e.g., HiREST (2303.16406)) pose new problems for temporal reasoning and dataset quality.
Human-like memory and retrieval: Inspired by cognitive structure, models such as HiCM $^2$ (2412.14585) construct hierarchical, compact memory banks and hierarchical recall modules to emulate multi-level human memory recall, demonstrating improved dense captioning.
Rich semantic control and personalization: Exemplar-based syntax conditioning (2112.01062) and user-controllable generation represent new frontiers.

The field is expected to benefit from releases of large-scale, hierarchically annotated datasets (e.g., Ego4D-HCap (2402.13250)), new evaluation schemes that measure structure as well as content, and further integration of multimodal reasoning and pretraining.

7. Comparative Landscape and Summary Table

Approach	Temporal Hierarchy	Linguistic Hierarchy	Supervision Strategy	Distinctive Features
h-RNN	✓	paragraph/sentence	sequence NLL	Dual-level RNN, attention, sentence-to-paragraph state
HRNE	✓ (chunked)	sentence	sequence NLL, attention	Hierarchical LSTM encoding, local-global dependency
hLSTMat	✓	sentence	attention, gating	Adaptive attention, visual/non-visual word modulation
HRL (RL)	✓ (manager/worker)	clause/phrase	RL (CIDEr), segmentation	Subgoal policy, clause detection, RL metrics
HMN	✓	entity/predicate/sent	modular, multi-level losses	Explicit cross-modal semantic modules
Boundary-aware	✓ (segmentation)	phrase/sentence	boundary-aware LSTM	Learned boundaries, chunked attention, soft/hard resets
Video ReCap	✓ (recursive/all)	sentence/segment/summary	curriculum, recursion	Flexible, scalable to hours-long video, recursion
HiCM $^2$	✓ (memory)	event/summary	hierarchical recall	Human-like memory banks, LLM-based summarization

Hierarchical video captioning thus defines a substantial amplification of video understanding capacity—moving from flat, clip-level sentence generation to structured, contextually rich, and scalable natural language descriptions, leveraging architectural, optimization, and annotation advances to address real-world complexity.