Weakly Supervised Natural Language Localization Networks for Video Proposal Prediction
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Solution Overview
Problem
Conventional natural language localization (NLL) models require high human annotation costs due to the need for temporal coordinates of video segments, making them inefficient for training with untrimmed videos.
Innovation Solution
The development of a weakly supervised natural language localization (WSNLL) approach that trains neural networks using video-sentence pairs without requiring temporal annotations, allowing for the localization of events in untrimmed videos based on arbitrary text queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NLL models use temporal coordinates for training, then localization accuracy is improved, but human annotation cost increases significantly
Solution Approach 1:
The patent uses sentence embeddings as a proxy representation for temporal coordinates. Instead of requiring actual temporal annotation data, the system copies the functional role of temporal coordinates by using semantic representations from sentence embeddings to guide the localization process, thereby reducing annotation costs while maintaining accuracy
Solution Approach 2:
The patent introduces sentence embeddings as an intermediary between the text query and the video localization task. These embeddings serve as a mediator that captures temporal information implicitly through semantic relationships, eliminating the need for direct temporal annotations while preserving the necessary localization guidance
2Reliability
If strongly supervised models use temporal annotations, then training performance is improved, but data preparation complexity increases
Solution Approach 1:
The system performs self-service by automatically generating training data through sentence embedding extraction and video-sentence pair construction. The model leverages existing video captions and queries to create training datasets without requiring manual temporal annotation, thereby simplifying data preparation while maintaining training effectiveness
Solution Approach 2:
The patent performs preliminary action by pre-extracting sentence embeddings and creating video-sentence pairs before the actual localization training. This preliminary processing of linguistic and semantic data eliminates the need for complex temporal annotation during data preparation, reducing overall system complexity
3Ease of manufacture
If weakly supervised approaches eliminate temporal annotations, then annotation cost is reduced, but localization precision may deteriorate
Solution Approach 1:
The patent changes the parameter representation from explicit temporal coordinates to implicit sentence embedding representations. By transforming the supervision signal from temporal parameters to semantic parameters, the system maintains localization precision while achieving weakly supervised training with minimal annotation requirements
Data Source
AI summary
Systems and methods are provided for weakly supervised natural language localization (WSNLL), for example, as implemented in a neural network or model. The WSNLL network is trained with long, untrimmed videos, i.e., videos that have not been temporally segmented or annotated. The WSNLL network or model defines or generates a video-sentence pair, which corresponds to a pairing of an untrimmed video with an input text sentence. According to some embodiments, the WSNLL network or model is implemented with a two-branch architecture, where one branch performs segment sentence alignment and the other one conducts segment selection. These methods and systems are specifically used to predict how a video proposal matches a text query using respective visual and text features.