Video LLM Temporal Localization Using Time and SlowFast Tokens
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Solution Overview
Problem
Conventional Video Large Language Models (Vid-LLMs) struggle with accurate temporal localization, particularly when answering 'when?' questions, due to issues in time representation, architecture, and data, leading to inaccurate timestamp learning and hallucination of irrelevant information.
Innovation Solution
Introduce time tokens for relative timestamp representation, SlowFast tokens for fine temporal resolution, and a Reasoning Temporal Localization (RTL) task with ActivityNet-RTL dataset, along with soft cross entropy loss and dynamic sampling strategies, to enhance temporal localization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plain text timestamps are used for time representation, then the model can process temporal information, but the temporal localization accuracy deteriorates because the model lacks access to frame rate information
Solution Approach 1:
The patent introduces time tokens as an intermediary representation that encodes temporal position information in a frame-rate invariant manner. These time tokens serve as a mediator between the video frames and the LLM, allowing the model to understand temporal relationships without directly accessing frame rate information. The time tokens are generated based on the position of frames in the video sequence and are processed by the LLM to enable accurate temporal localization.
2Measurement precision
If uniform sampling of frames is used in the architecture, then the model complexity is reduced, but the temporal resolution deteriorates making it insufficient for accurate temporal localization
Solution Approach 1:
The patent employs dynamic frame sampling where the number of frames sampled at different temporal resolutions is adjusted based on the specific video and task requirements. The architecture dynamically selects between high-temporal-resolution sampling (for actions requiring precise timing) and low-temporal-resolution sampling (for broader contextual understanding), allowing the model to adapt its temporal resolution needs rather than using a fixed uniform sampling rate.
3Measurement precision
If temporal localization data is excluded from training data, then the data diversity is improved, but the temporal localization capability deteriorates
Solution Approach 1:
The patent performs preliminary processing of video data to generate accurate temporal annotations and time tokens before feeding the data to the LLM. This preliminary action includes verifying timestamp accuracy, generating frame position information, and creating time token sequences that encode temporal relationships. By preparing the temporal information in advance, the model can focus on learning temporal localization patterns without the complexity of verifying raw timestamp data during training.
4Measurement precision
If standard projection layers are used to project visual tokens to LLM input space, then the implementation is simplified, but the temporal localization accuracy deteriorates
Solution Approach 1:
The patent applies different projection strategies to different types of visual tokens based on their specific characteristics and requirements. Time tokens undergo specialized temporal encoding projections that preserve temporal position information, while content tokens use standard projection methods. This localized quality approach ensures that temporal information is handled with appropriate precision while avoiding unnecessary complexity in the overall architecture.
Data Source
AI summary
Embodiments of the present disclosure relate to language instructed temporal localization in videos, and provide multimodal large language models (LLMs) for performing language instructed temporal localization in video, as well as methods for training and implementing such models. In contrast to conventional systems, models according to embodiments of the present disclosure are designed to answer “when?” questions, while simultaneously improving other relevant capabilities of multimodal LLMs. Additionally, and/or alternatively, embodiments of the present disclosure may utilize a soft cross entropy loss and/or a dynamic sampling strategy to further improve the model, which allows the model to better understand temporal information and perform event localization tasks. For example, embodiments of the present disclosure may perform a dynamic sampling strategy and utilize video tokens and image tokens and/or utilize a soft cross entropy loss that applies a Gaussian distribution to the loss.