Predicting Episodic Video Memorability using Deep Features Fusion Strategy
Bibliographic record
Abstract
Video memorability prediction has become an important research topic in computer vision in recent years. The movie's input is highly remembered that gains much attention with unbounded time constraints. Episodic memory is a fascinating research area that needs much attention using video processing tools and techniques. Episodic memories are long-lasting with complete detail. Movies are one of the best instances of episodic memory. This paper proposes a novel framework to fuse deep features to predict the probability of recalling episodic events. Memories are reproducible and sensitive to sophisticated set of properties rather than low-level propertiesthe proposed framework pin up the fusion of text, visual and motion features. A fuzzy-based FastText model, a supervised text extraction module, is designed to extract the annotations with their relevant classes. The colour histogram analysis is done to determine the dominant colour region that performs as a connected fragment to form episodic video sequences. A novel Faster R-CNN is designed to discover the scene objects using an informative regional proposal network formation. Here, the modified loss function sorts out the lowest overlapping regions yielding the best proposals. The ‘high-level’ properties are collected using Principal Component Analysis (PCA) to form episodic shots. These are fused to estimate the memorability score. The proposed framework is implemented in Mediaeval 2018 datasets. A superior spearman's rank correlation result is achieved as 0.6428 short-term and 0.4285 long-term memorability than the latest comparable methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".