Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.
Code (0)
등록된 구현이 없습니다.
Tasks
Action LocalizationBenchmarkingComputational EfficiencyTemporal Action LocalizationSimilar Papers 제목 키워드 기반
Benchmarking Deep Neural Networks for Modern Recommendation Systems
This paper presents a requirement-oriented benchmark of seven deep neural architectures, CNN, RNN, GNN, Autoencoder, Transformer, Neural Collaborative Filtering, and Siamese Networks, across three real-world datasets: Re…
Computational EfficiencyCollaborative FilteringRecommendation SystemsBenchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions
State Space Models (SSMs) have emerged as a promising alternative to Transformers for long-context sequence modeling, offering linear $O(N)$ computational complexity compared to the Transformer's quadratic $O(N^2)$ scali…
Computational EfficiencyTinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learnin…
Human Activity RecognitionComputational EfficiencyBenchmarking the Robustness of Spatial-Temporal Models Against Corruptions
The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing). While much progress h…
BenchmarkingComputational EfficiencyVideo UnderstandingBenchmarking Federated Machine Unlearning methods for Tabular Data
Machine unlearning, which enables a model to forget specific data upon request, is increasingly relevant in the era of privacy-centric machine learning, particularly within federated learning (FL) environments. This pape…
BenchmarkingComputational EfficiencyFederated LearningMachine Unlearning+1