Per-Clip Video Object Segmentation With Progressive Memory
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Solution Overview
Problem
Video object segmentation is challenging due to drastic changes in object appearance caused by occlusions and drifts, especially as the length of the video sequence increases, leading to inefficiencies in existing frame-by-frame processing methods.
Innovation Solution
A digital design system performs per-clip object segmentation using a trained encoder-decoder network that processes video sequences in clips, leveraging a progressive memory matching process and intra-clip refinement to enhance accuracy and efficiency by accessing nearby frames and utilizing transformer-based attention for spatiotemporal correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame-by-frame processing is used for video object segmentation, then processing simplicity is maintained, but segmentation accuracy deteriorates due to inability to capture temporal context and object appearance changes
Solution Approach 1:
The video sequence is divided into multiple clips, where each clip contains a set of consecutive frames. This segmentation allows the model to process temporal context within each clip while maintaining manageable computational complexity. The encoder-decoder network processes each clip independently, capturing object appearance changes and occlusions within the temporal window of each clip.
Solution Approach 2:
The approach transitions from processing individual frames in isolation to processing sequences of frames (temporal dimension). By incorporating multiple frames per clip, the model captures temporal dynamics and object appearance changes over time, significantly improving segmentation accuracy while using efficient neural network architectures.
2Duration of action of moving object
If the video sequence length increases, then more complete object information is captured, but segmentation reliability deteriorates due to object appearance changes and occlusions
Solution Approach 1:
The encoder-decoder network is pre-trained on large datasets to learn robust features for handling occlusions and object appearance changes. This preliminary training enables the model to maintain high segmentation reliability even when processing longer video sequences where objects may be occluded or undergo significant appearance changes.
Solution Approach 2:
The decoder uses feedback from the encoder's extracted features to generate accurate segmentation masks. The encoder captures temporal context and object dynamics across multiple frames within a clip, and the decoder refines this information to produce reliable segmentation even under challenging conditions like occlusions and appearance changes.
3Measurement precision
If clip-by-clip processing with memory updates is implemented, then segmentation accuracy is improved through temporal context, but computational efficiency deteriorates
Solution Approach 1:
The video is divided into clips that can be processed in parallel batches. Within each clip, frames are processed to capture temporal context, but the clip-level processing allows for efficient batch computation. This segmentation strategy balances the need for temporal context with computational efficiency by limiting the temporal window while enabling parallel processing across multiple clips.
Data Source
AI summary
Embodiments are disclosed for performing per-clip object segmentation of objects in a video sequence using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a query video sequence and memory data, the memory data including a memory video frame from the query video sequence and an annotated memory video frame including an object mask for an object in the memory video frame, segmenting the query video sequence into a plurality of query video clips and passing a first set of query video frames of a first query video clip and the memory data through a trained encoder-decoder network, predicting a modified set of query video frames, including predictions of object masks for the object, and updating the memory data to include one or more frames of the first set of query video frames and the modified set of query video frames.