Video Object Detection via Attention Models
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Solution Overview
Problem
Existing video salient object detection (VSOD) techniques face limitations in performance due to the representation ability of low-level features and high computational costs, especially when modeling temporal information, and accumulate errors over time.
Innovation Solution
An end-to-end trainable VSOD framework is introduced, utilizing two attention models: a non-local self-attention model for capturing global and long-range spatiotemporal dependencies, and a cross-level co-attention model for learning correlations between low-level and high-level features, along with a contrastive learning technique to differentiate between foreground and background regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing VSOD techniques use low-level features and optical flow to model temporal information, then object detection capability is provided, but computational cost is high and performance is limited by representation ability
Solution Approach 1:
The patent replaces traditional optical flow computation and hand-crafted low-level feature extraction with a deep learning-based feature extraction network that automatically learns hierarchical features from video frames, substituting mechanical computation with intelligent learning systems
Solution Approach 2:
The patent transforms the feature representation parameters from low-level hand-crafted features to high-level deep learning features, changing the parameter space to achieve better representation ability while maintaining computational efficiency through end-to-end training
2Measurement precision
If CNN-based models are used to model temporal information in videos, then detection accuracy improves, but computational cost increases significantly
Solution Approach 1:
The patent segments the video processing task into independent frame-level feature extraction and temporal relationship modeling components, allowing parallel processing of individual frames while reducing the computational burden of temporal modeling
Solution Approach 2:
The patent uses feature pyramids that copy and reuse features at multiple scales and levels, avoiding redundant computation by reusing extracted features across different processing stages and temporal contexts
3Extent of automation
If existing techniques process video data with temporal dependencies, then object tracking capability is achieved, but errors accumulate over time
Solution Approach 1:
The patent incorporates feedback mechanisms where detection results from previous frames inform current frame processing, but with reset capabilities that prevent error accumulation by periodically re-evaluating temporal relationships and correcting drift
Solution Approach 2:
The patent prepares multiple feature representations and alternative detection paths in advance that can compensate for errors, providing cushioning against error accumulation through pre-computed fallback options and ensemble methods
Data Source
AI summary
The present disclosure describes techniques of detecting objects in a video. The techniques comprises extracting features from each frame of the video; generating a first attentive feature by applying a first attention model on at least some of features extracted from any particular frame among the plurality of frames, wherein the first attention model identifies correlations between a plurality of locations in the particular frame by computing relationships between any two locations among the plurality of locations; generating a second attentive feature by applying a second attention model on at least one pair of features at different levels selected from the features extracted from the particular frame, wherein the second attention model identifies a correlation between at least one pair of locations corresponding to the at least one pair of features; and generating a representation of an object included in the particular frame.