Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding Article Swipe
Mohamed Afham
,
Satya Narayan Shukla
,
Omid Poursaeed
,
Pengchuan Zhang
,
Ashish S. Shah
,
Ser-Nam Lim
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.11569
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.11569
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length. A common approach to process long videos is applying a short-form video model over uniformly sampled clips of fixed temporal length and aggregating the outputs. This approach neglects the underlying nature of long videos since fixed-length clips are often redundant or uninformative. In this paper, we aim to provide a generic and adaptive sampling approach for long-form videos in lieu of the de facto uniform sampling. Viewing videos as semantically consistent segments, we formulate a task-agnostic, unsupervised, and scalable approach based on Kernel Temporal Segmentation (KTS) for sampling and tokenizing long videos. We evaluate our method on long-form video understanding tasks such as video classification and temporal action localization, showing consistent gains over existing approaches and achieving state-of-the-art performance on long-form video modeling.
Related Topics
Concepts
Computer science
Segmentation
Artificial intelligence
Task (project management)
Kernel (algebra)
CLIPS
Scalability
Sampling (signal processing)
Process (computing)
Computer vision
Pattern recognition (psychology)
Filter (signal processing)
Mathematics
Management
Economics
Combinatorics
Operating system
Database
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.11569
- https://arxiv.org/pdf/2309.11569
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386977284
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386977284Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.11569Digital Object Identifier
- Title
-
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video UnderstandingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-20Full publication date if available
- Authors
-
Mohamed Afham, Satya Narayan Shukla, Omid Poursaeed, Pengchuan Zhang, Ashish S. Shah, Ser-Nam LimList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.11569Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2309.11569Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.11569Direct OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Task (project management), Kernel (algebra), CLIPS, Scalability, Sampling (signal processing), Process (computing), Computer vision, Pattern recognition (psychology), Filter (signal processing), Mathematics, Management, Economics, Combinatorics, Operating system, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.neglects | 51 |
| abstract_inverted_index.outputs. | 48 |
| abstract_inverted_index.sampling | 77, 111 |
| abstract_inverted_index.scalable | 102 |
| abstract_inverted_index.segments | 20 |
| abstract_inverted_index.temporal | 43, 130 |
| abstract_inverted_index.variable | 22 |
| abstract_inverted_index.achieving | 140 |
| abstract_inverted_index.formulate | 97 |
| abstract_inverted_index.long-form | 80, 121, 144 |
| abstract_inverted_index.modeling. | 146 |
| abstract_inverted_index.redundant | 63 |
| abstract_inverted_index.sampling. | 89 |
| abstract_inverted_index.segments, | 95 |
| abstract_inverted_index.uniformly | 38 |
| abstract_inverted_index.approaches | 138 |
| abstract_inverted_index.consistent | 19, 94, 134 |
| abstract_inverted_index.real-world | 10 |
| abstract_inverted_index.short-form | 34 |
| abstract_inverted_index.tokenizing | 113 |
| abstract_inverted_index.underlying | 53 |
| abstract_inverted_index.aggregating | 46 |
| abstract_inverted_index.performance | 142 |
| abstract_inverted_index.short-range | 8 |
| abstract_inverted_index.Segmentation | 108 |
| abstract_inverted_index.fixed-length | 59 |
| abstract_inverted_index.semantically | 18, 93 |
| abstract_inverted_index.localization, | 132 |
| abstract_inverted_index.understanding | 4, 123 |
| abstract_inverted_index.unsupervised, | 100 |
| abstract_inverted_index.classification | 128 |
| abstract_inverted_index.task-agnostic, | 99 |
| abstract_inverted_index.uninformative. | 65 |
| abstract_inverted_index.state-of-the-art | 141 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |