PaperlayerView exact revision on arXiv

Source-linked AI summary

Drop the Details, Get the Gist: Dynamic Conceptual Representation for Video Recognition

Ryoya Yoshiyama, Bowen Wang, Yuta Nakashima

arXiv:2610.36832v1cs.CV

TL;DR

Video recognition models can be difficult to interpret, and extending concept-based representations to video requires accounting for temporal order. The paper represents procedural videos as sequences of segment-level concept scores for Transformer classification; experiments find comparable accuracy across concept granularities, with coarse concepts potentially sufficient.

  • Problem

    Video concept-based interpretability remains challenging because video recognition must account for which concepts appear and when they appear across time.

    Source: §1 · pp. 1–2

  • Method

    The framework extracts concepts from procedural-step descriptions, represents video segments with concept scores, and uses a Transformer to classify their temporal sequence.

    Source: §3 · p. 5, §5 · p. 14

  • Results

    With k ≥10, concept granularity has limited effect on accuracy on YouCook2 and ActivityNet; the model achieves 89.5% and 93.5% accuracy, versus black-box baselines at 90.7% and 94.3%, respectively.

    Source: §1 · p. 3, §5 · p. 14

  • Takeaways & Limitations

    Simple, coarse-grained concepts may be sufficient for procedural video recognition, without relying heavily on fine-grained linguistic structure.

    Source: §1 · p. 3, §5 · p. 14

    Accuracy and explanations inherit the video-language model’s errors and biases, and concept-importance scores do not separate interactions or guarantee causal interpretation.

    Source: §5 · p. 14

Abstract

from arXiv · show

Concept Bottleneck Models (CBMs), which route predictions through a layer of human-interpretable concepts, have substantially improved the interpretability of visual classification by exposing an explicit, language-based rationale for each prediction. Most existing work focuses on static images, and temporal dynamics in videos have been underexplored in conceptual representation. We propose a framework that extends concept-based image recognition to procedural video understanding. It represents a multi-step video as a temporal sequence of segment-level concept score vectors and feeds this sequence into a Transformer encoder to predict the class label. We also explore a way to estimate the contribution of each concept for further interpretability. We evaluated three concept sets of different granularities on cooking and household-activity videos and found that coarser-grained concepts are sufficient for classifying these videos, which implies that fine-grained textual descriptions may be unnecessary.

1 Introduction

Human video understanding relies on recognizing ordered semantic events, motivating video models that reason over interpretable concepts rather than opaque raw features. The paper applies a temporal concept-based framework to procedural videos and compares concept sets of different granularity.

1 Introduction

  • Video concept models must account for both which concepts appear and when they appear, across variable-length videos and long-range dependencies.The introduction frames temporal extension of concept-based interpretability as an open challenge beyond static-image CBMs.Source: §1 · p. 2
  • The model represents each video segment with concept scores and feeds their temporal sequence to a Transformer, which learns when concepts appear.Concepts are selected from natural-language descriptions of procedural steps.Source: §1 · p. 2
  • The paper evaluates textual supervision on two datasets of multi-step procedural videos as a pilot study of concepts for video recognition.The study compares three concept sets, from detailed descriptions to simple object-and-action concepts.Source: §1 · p. 3
  • 89.5% on YouCook2 and 93.5% on the Household Activities subset of ActivityNet, versus black-box baselines at 90.7% and 94.3%, respectively.The experiments also find comparable accuracy across concept granularities, suggesting coarse concepts may suffice for procedural video recognition.Source: §1 · p. 3

2 Related Work

Related work covers input-region explanations, concept-based visual representations, and video-language models. This study uses video-language alignment to extract concepts and compares their granularity for procedural video classification.

2.1 Post-Hoc Explanation Methods

  • Region-based explanations highlight important input locations, whereas concept-based explanations report recognized linguistic concepts as a complementary view of a decision.Prior methods include gradient-based localization, attention maps, and perturbation-based masking.Source: §2.1 · p. 3

2.2 Concept-Based Representations of Visual Information

  • Concept-based models make decisions inspectable through explicit concepts; this study compares concept granularities for procedural videos with many temporally ordered sub-events.Prior work includes post-hoc concept probes, CBMs, label-free CBMs, and video concept approaches.Source: §2.2 · pp. 3–4

2.3 Video Understanding with Vision-Language Models

  • Video-language models align video and text representations, and this work uses their alignment to extract intermediate concepts rather than classify directly.Segment-to-concept similarities become inputs to the temporal model.Source: §2.3 · p. 4

3 Method

The method represents procedural videos through concepts drawn from step descriptions, then models their temporal scores with a Transformer. It also estimates each concept’s influence by masking it and measuring the change in the prediction score.

3 Method

  • Step descriptions from training videos supply the dataset-level concepts used to represent procedural videos over time.Each video is divided into overlapping fixed-duration segments, and a Transformer predicts its class from the sequence of segment-level concept scores.Source: §3 · pp. 4–5

3.1 Concept Set Construction

  • Three concept sets span verbatim step descriptions, extracted verb phrases, and the coarsest object-and-action concepts.Qwen3 generates concepts from descriptions for the latter two sets; similarity-based filtering removes redundant concepts.Source: §3.1 · pp. 5–7

3.2 Temporal Concept Score Computation

  • Each segment–concept score is the cosine similarity between embeddings from pretrained video and text encoders.Together, the scores form a matrix whose rows track each concept over time and whose columns show concept activations at each time.Source: §3.2 · p. 7

3.3 Transformer-Based Classification

  • A Transformer aggregates the temporal concept scores to predict the video class, with positional encodings supplying temporal order.A learnable classification token aggregates video-level information; the model uses stacked self-attention and feed-forward blocks.Source: §3.3 · pp. 7–8

3.4 Interpreting Predictions via Concept Importance

  • Concept importance is the change in ground-truth-class prediction score when that concept’s scores are zeroed across all segments.Positive values indicate support for the prediction, negative values indicate opposition, and concepts are ranked by absolute importance.Source: §3.4 · pp. 8–9
  • Single-concept masking does not separate interactions mediated by Transformer self-attention, so importance is not a strict causal attribution.Jointly masking multiple concepts may have an effect different from the sum of their individual values.Source: §3.4 · p. 9

4 Experiments

The concept-bottleneck models retain accuracy close to direct-video baselines, while accuracy is largely insensitive to concept granularity when at least 10 concepts are retained. Interpretability examples, temporal-modeling comparisons, and a generic-concept experiment further examine the model’s rationales and design choices.

4.1 Experimental Setup

  • Experiments cover YouCook2’s 89 recipe categories and ActivityNet’s 44 Household Activities classes, using video descriptions as concept sources.YouCook2 uses step descriptions; ActivityNet uses per-video captions.Source: §4.1 · p. 9

Performance Evaluation.

  • The concept-bottleneck models reach 89.5% on YouCook2 and 93.5% on ActivityNet, just 1.2 and 0.8 points below direct-video baselines.The baseline reaches 90.7% on YouCook2 and 94.3% on ActivityNet.Source: Performance Evaluation · p. 10
  • With k ≥10, accuracy is largely insensitive to concept-set granularity on both datasets, and detailed descriptions do not meaningfully outperform object-and-action concepts.The authors speculate that core verbs and objects carry more signal than fine-grained grammatical details.Source: Performance Evaluation · pp. 10–11
  • Across all three concept sets and both datasets, Top-k filtering outperforms using all concepts; accuracy saturates between k = 20 and k = 40, while k = 5 is often too restrictive.The authors identify k = 20 as the best balance between coverage and noise suppression.Source: Performance Evaluation · p. 11

Interpretability.

  • For correctly classified recipe videos, high-importance concepts capture key steps such as dissolving miso or combining meatballs with sauce.The rationale examples use Craw with k = 20.Source: Interpretability · pp. 11–12, Figure 4 · p. 11
  • For a bulgogi video misclassified as galbi, all five highest-ranked concepts have negative qi and describe galbi or short ribs, revealing evidence against the ground-truth class.The paper suggests similar ingredients and preparation steps may contribute to confusion between the two dishes.Source: Interpretability · pp. 12–13, Figure 5 · p. 12

Effect of Temporal Modeling.

  • On YouCook2 with Coa and Top-20 filtering, Transformer temporal modeling improves Top-1 accuracy by roughly 3 points over temporal average pooling.The authors suggest the pooled variant remains strong because recipe categories can often be predicted from ingredients or utensils, while the Transformer also models concept order.Source: Effect of Temporal Modeling · p. 13, Table 3 · p. 13

Effect of the Concept Source.

  • On YouCook2, the generic concept set achieves 86.6% accuracy versus 89.5% for dataset-specific Coa, a drop of about 3 points.The sets differ in size (1,895 versus 8,973 concepts), so the gap may reflect both concept source and set size.Source: Effect of the Concept Source · pp. 13–14, Table 4 · p. 13

5 Conclusion

The framework routes procedural-video predictions through temporal concept scores and uses a Transformer for recognition. Experiments found comparable accuracy across concept granularities, while the authors note limitations in its VLM dependence, concept-importance scores, and domain-specific text.

5 Conclusion

  • The framework uses a Transformer to classify videos from temporal sequences of human-interpretable concept scores, addressing the black-box nature of video recognition.The authors estimate each concept's contribution as an additional interpretability measure.Source: §5 · p. 14
  • With k ≥10, detailed descriptions and simpler object-and-action concepts achieve comparably high accuracy on YouCook2 and ActivityNet.The framework may therefore rely less on fine-grained linguistic structure than on temporal evolution of core semantic units.Source: §5 · p. 14
  • Predictions and explanations inherit errors and biases from the VLM, while zero-baseline masking does not separate concept interactions or guarantee causal interpretation.The authors also report a measurable benefit from domain-specific text, so performance may degrade where such descriptions are unavailable.Source: §5 · p. 14
  • Future work includes grounding concepts in video regions or segments, user studies of explanation quality, dynamic concept selection, and evaluation on other video types.The examples of other video types include short single-action clips.Source: §5 · p. 14

A Prompts for Concept Generation

The appendix describes prompts for constructing concept sets from step descriptions, alongside a dataset-agnostic LLM prompt. The sets range from verbatim descriptions to extracted verb phrases and decomposed object-and-action concepts.

A Prompts for Concept Generation

  • Craw uses each raw step description verbatim, Cverb extracts verb phrases, and Coa separately extracts actions and objects from each step description.The same prompts are used for YouCook2 and ActivityNet; the dataset-agnostic set instead uses one open-ended prompt without dataset text.Source: A Prompts for Concept Generation · pp. 19, 23, 25
  • The Coa action prompt extracts verb-object pairs, drops prepositional phrases, resolves omitted or pronominal objects, and separates multiple verbs or objects.It keeps verb particles such as “pick up” but drops prepositional phrases such as “in the bowl.”Source: A Prompts for Concept Generation · p. 20
  • The Coa object prompt extracts nouns while excluding verbs, pronouns, time expressions, adjectives, and measurement or quantity words.For “X of Y,” it keeps the semantic head Y, as in “piece of butter” becoming “butter.”Source: A Prompts for Concept Generation · pp. 23–24
  • The dataset-agnostic prompt asks for over 1,000 cooking-related actions or ingredients without referencing dataset text.The appendix examples show generalized action strings, including “chop a vegetable” and “take off a coat.”Source: A Prompts for Concept Generation · pp. 19, 20, 25

Related papers

Loading 2610.36832v1…