A Context-Aware Loss Function for Action Spotting in Soccer Videos
In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation.
Code (1)
Tasks
Action SpottingVideo UnderstandingSimilar Papers 제목 키워드 기반
SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting
Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, provid…
Sign Language RecognitionRepresentation LearningTemporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts
Toward the goal of automatic production for sports broadcasts, a paramount task consists in understanding the high-level semantic information of the game in play. For instance, recognizing and localizing the main actions…
Action SpottingSign Spotting Disambiguation using Large Language Models
Sign spotting, the task of identifying and localizing individual signs within continuous sign language video, plays a pivotal role in scaling dataset annotations and addressing the severe data scarcity issue in sign lang…
Sign Language TranslationPrototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spottin…
General Classificationimbalanced classificationKeyword SpottingTransfer LearningGLASS: Global to Local Attention for Scene-Text Spotting
In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tasks are accomplished by operating over a …
Text DetectionText Spotting