Extracting and Reasoning over Object Behaviors to Recognize Adverb Types in Video Clips

Conceitos essenciais

A novel framework that extracts object-behavior facts from video clips, reasons over those facts using transformers, and predicts the adverb types that best describe the overall video content.

Resumo

The key highlights and insights of this content are:

1. The authors propose a new framework for adverb-type recognition in video clips, which consists of three phases: Extraction, Reasoning, and Prediction.

2. In the Extraction phase, the framework extracts discrete object-behavior facts from raw video clips using a pipeline that detects objects, computes their optical flow, and represents the information in an Answer Set Programming (ASP) format.

3. For the Reasoning phase, the authors explore two approaches: a single-step symbolic-based baseline that learns indicator rules using FastLAS, and a novel transformer-based method that performs masked language modeling over the extracted object-behavior facts to learn summary representations.

4. In the Prediction phase, the framework uses the learned object-behavior representations, concatenated with action-type embeddings, to train separate SVM classifiers to distinguish between each adverb and its antonym.

5. The authors release two new datasets, MSR-VTT-ASP and ActivityNet-ASP, which contain the extracted object-behavior facts and adverb annotations for subsets of the MSR-VTT and ActivityNet video datasets.

6. Experimental results show that the transformer-based reasoning approaches outperform the previous state-of-the-art methods on the MSR-VTT-ASP and ActivityNet-ASP datasets, demonstrating the effectiveness of reasoning over object behaviors for adverb-type recognition.

Estatísticas

"We process clips where both raw-footage and adverb-annotations are available using our Extraction Phase (Section 3.1), to obtain 1309 ASP-programs for our new MSR-VTT-ASP dataset and 1751 ASP-programs for our new ActivityNet-ASP dataset."
"Each program is labeled with one or more of 22 adverb-types (11 adverb/antonym pairs) according the source clip's labels."

Citações

"In this work, following the intuition that adverbs describing scene-sequences are best identified by reasoning over high-level concepts of object-behaviors, we propose the design of a new framework that reasons over object-behaviour-facts extracted from raw-video-clips to recognize the clip's corresponding adverb-types."
"Importantly, unlike previous work for general scene adverb-recognition (Doughty et al., 2020; Doughty & Snoek, 2022), our framework does not rely on I3D encodings during training or inference."

Principais Insights Extraídos De

Reasoning over the Behaviour of Objects in Video-Clips for Adverb-Type Recognition

by Amrit Diggav... às arxiv.org 03-29-2024

https://arxiv.org/pdf/2307.04132.pdf

Perguntas Mais Profundas

What other types of high-level object behavior representations could be explored beyond the ones used in this work, and how might they impact adverb-type recognition performance

In addition to the high-level object behavior representations explored in the work, other types could be considered to enhance adverb-type recognition performance. One potential approach could involve incorporating spatial relationships between objects in a scene. By analyzing how objects interact spatially, such as proximity, orientation, or grouping, the framework could capture more nuanced behaviors that contribute to adverb recognition. For example, if a person is "walking slowly towards a table," the spatial relationship between the person and the table could provide valuable context for understanding the adverb "slowly."
Another aspect to explore is temporal relationships between object behaviors. By analyzing the sequence of actions and interactions between objects over time, the framework could capture dynamic patterns that correspond to different adverb types. For instance, observing a series of actions like "pouring slowly, stirring gently, and mixing thoroughly" could provide rich temporal information for recognizing adverbs related to the manner of actions.
Furthermore, incorporating contextual information such as scene semantics or object affordances could also enhance the representation of object behaviors. Understanding the context in which actions take place and how objects interact within that context can provide valuable cues for adverb recognition. By considering these additional dimensions of object behavior, the framework could improve its ability to recognize a wider range of adverb types with higher accuracy.

How could the proposed framework be extended to handle more complex adverb types or combinations of adverbs in a single video clip

To handle more complex adverb types or combinations of adverbs in a single video clip, the proposed framework could be extended in several ways. One approach is to introduce hierarchical reasoning mechanisms that can capture dependencies between different adverbs. By hierarchically organizing adverb types based on their semantic relationships or temporal dependencies, the framework could reason over multiple levels of abstraction to interpret complex adverb combinations.
Another extension could involve incorporating multi-modal information, such as audio cues or textual descriptions, to provide additional context for adverb recognition. By integrating multiple sources of information, the framework could leverage complementary signals to disambiguate complex adverb types or combinations more effectively.
Furthermore, the framework could benefit from incorporating reinforcement learning techniques to adaptively adjust the reasoning process based on feedback from the adverb recognition task. By allowing the system to learn from its mistakes and refine its reasoning strategies over time, it could improve its ability to handle complex adverb types and combinations in a more dynamic and adaptive manner.

Could the object-behavior extraction and reasoning approaches be applied to other video understanding tasks beyond adverb recognition, such as action recognition or video captioning

The object-behavior extraction and reasoning approaches proposed in the work could be applied to various other video understanding tasks beyond adverb recognition. For instance, in action recognition, the framework could analyze object behaviors to identify patterns and sequences of actions that correspond to specific activities. By reasoning over the dynamics of object interactions and movements, the system could improve its accuracy in recognizing complex actions or activities in videos.
Similarly, in video captioning, the extracted object behaviors could serve as valuable input for generating descriptive captions that accurately reflect the content of the video. By leveraging the rich information captured in object behaviors, the framework could enhance the quality and specificity of generated captions, providing more detailed and informative descriptions of video content.
Overall, the object-behavior extraction and reasoning approaches have the potential to enhance various video understanding tasks by providing a structured and interpretable representation of object interactions and dynamics in video clips. By leveraging this information, systems can improve their performance in tasks requiring detailed analysis of video content.