REVIEW 3 cited by
F$^3$Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Analyzing Fast, Frequent, and Fine-grained (F$^3$) events presents a significant challenge in video analytics and multi-modal LLMs. Current methods struggle to identify events that satisfy all the F$^3$ criteria with high accuracy due to challenges such as motion blur and subtle visual discrepancies. To advance research in video understanding, we introduce F$^3$Set, a benchmark that consists of video datasets for precise F$^3$ event detection. Datasets in F$^3$Set are characterized by their extensive scale and comprehensive detail, usually encompassing over 1,000 event types with precise timestamps and supporting multi-level granularity. Currently, F$^3$Set contains several sports datasets, and this framework may be extended to other applications as well. We evaluated popular temporal action understanding methods on F$^3$Set, revealing substantial challenges for existing techniques. Additionally, we propose a new method, F$^3$ED, for F$^3$ event detections, achieving superior performance. The dataset, model, and benchmark code are available at https://github.com/F3Set/F3Set.
Forward citations
Cited by 3 Pith papers
-
FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
A new badminton video dataset with action, tactic, and decision-level annotations, a 12-task benchmark, and a baseline showing hit-centric keyframes plus coordinate-guided compression improve MLLM performance.
-
Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles
A tennis doubles annotation framework is built and evaluated, showing transfer-learned CNNs outperform pose-only GCNs for automated shot and formation labeling.
-
Enhancing Sports Strategy with Video Analytics and Data Mining: Assessing the effectiveness of Multimodal LLMs in tennis video analysis
VideoLLaMA2's tennis sequence edit score jumps from 39.7 to 76.0 when text coordinates from detection models are included in the prompt, and a separately fine-tuned CLIP encoder raises single-event accuracy from 0.41 to 0.56.
Discussion (0). Sign in to comment.