Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.0K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.0K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

1.0K
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
1.0K
Convolution Properties I01:20

Convolution Properties I

640
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
640
Convolution Properties II01:17

Convolution Properties II

623
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
623
Parallel Processing01:20

Parallel Processing

823
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
823
Deconvolution01:20

Deconvolution

647
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
647

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhancing selective itaconic acid synthesis in Yarrowia lipolytica through targeted metabolite transport reprogramming.

Biotechnology for biofuels and bioproducts·2025
Same author

CoVR-2: Automatic Data Construction for Composed Video Retrieval.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Multi-Task Learning of Object States and State-Modifying Actions From Web Videos.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Mechanism of mitoribosomal small subunit biogenesis and preinitiation.

Nature·2022
Same author

Learning to Answer Visual Questions From Web Videos.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

Epitranscriptomics of Mammalian Mitochondrial Ribosomal RNA.

Cells·2020
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Videos

Long-Term Temporal Convolutions for Action Recognition.

Gul Varol, Ivan Laptev, Cordelia Schmid

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 11, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces long-term temporal convolutions (LTC) for improved human action recognition in videos. LTC-CNN models capture longer temporal dynamics, significantly boosting recognition accuracy on benchmark datasets.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Human action recognition is crucial for understanding video content.
    • Current convolutional neural network (CNN) methods often fail to capture the full temporal extent of actions.
    • Representations are typically learned from limited video frames, missing long-term dynamics.

    Purpose of the Study:

    • To develop a novel neural network architecture for enhanced video representation learning.
    • To improve the accuracy of human action recognition by modeling longer temporal extents.
    • To investigate the impact of different low-level video features on action recognition performance.

    Main Methods:

    • Utilized neural networks with long-term temporal convolutions (LTC).
    • Explored various low-level representations, including raw video pixels and optical flow.
    • Evaluated models on the UCF101 and HMDB51 human action recognition benchmarks.

    Main Results:

    • LTC-CNN models with extended temporal extents demonstrated superior action recognition accuracy.
    • High-quality optical flow estimation was found to be critical for accurate action modeling.
    • Achieved state-of-the-art results on UCF101 (92.7%) and HMDB51 (67.2%).

    Conclusions:

    • Long-term temporal convolutions are effective for capturing the full temporal structure of human actions.
    • The choice of low-level visual features significantly impacts the performance of action recognition models.
    • The proposed LTC-CNN approach advances the state-of-the-art in human action recognition.