VQA 2 : Visual Question Answering for Video Quality Assessment
This paper discusses a new approach to assessing video quality using advanced models that can answer questions about video content. It introduces a dataset specifically designed for this purpose and shows how these models can provide better insights into video quality.
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- 1 Each dimension in all the radar charts is normalized using different ranges based on the specific data characteristics to highlight performance differences.
- 2 Incorporating visual question answering to aid model training and inference has tremendous potential for development in low-level vision.
- 3 Many classic multi-modal works follow the well-established pretraining-finetuning paradigm, which has been proven to be an effective way for foundation model development.
- 4 The first format uses a "0/1" sequence to directly indicate stalling for each frame, where "1" represents stalling and "0" represents smooth playback.
Introduction
The VQA 2 series models perform on video quality scoring and video quality understanding tasks. The advent and proliferation of large multi-modal models (LMMs) have introduced new paradigms to computer vision and transformed various tasks into a unified visual question answering framework.
Video Quality Assessment (VQA) initially focused on quantitative video quality scoring.
Related work has not been explored in the video domain, leaving substantial room for improvement.
For example, q-align attains high precision and generalizability in video quality scoring.
We believe that extracting keyframes and motion features cannot accurately distinguish between longtime stalling/rebuffering (distinct from short-term stuttering/frame jitter) and the static-frame period during smooth playback.
Methodology
The paradigm of visual-language instruction tuning using multi-modal instruction datasets has markedly enhanced the performance of video LMMs in high-level visual tasks intimately related to video semantics such as video understanding and video temporal analysis. The UGC video quality scoring task involves datasets that contain various authentic or synthetic distortions.
Study Design
These models possess only the capability to score the video quality but almost entirely miss the function of video quality understanding and analysis, with no capability to provide reasonable responses to diverse question types.
They can not realize the boosting demand for quality understanding and analysis of spatial and temporal quality attributes in videos.
Results & Findings
Each dimension in all the radar charts is normalized using different ranges based on the specific data characteristics to highlight performance differences. Advances in LMMs drive Video Quality Assessment (VQA) toward more holistic visual quality understanding tasks.
- Each dimension in all the radar charts is normalized using different ranges based on the specific data characteristics to highlight performance differences.
- Advances in LMMs drive Video Quality Assessment (VQA) toward more holistic visual quality understanding tasks.
- Recent studies in the image domain have demonstrated that Visual Question Answering (VQA) can markedly enhance low-level visual quality evaluation.
- We present the VQA 2 series models leveraging this foundation.
- Incorporating visual question answering to aid model training and inference has tremendous potential for development in low-level vision.
This limitation slightly impacts the diversity of model outputs and the accuracy of responses to specific questions.
Each dimension in all the radar charts is normalized using different ranges based on the specific data characteristics to highlight performance differences.
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Practical Applications
You will receive a detailed depiction of the quality of a video, which may include evaluations and comments on various attributes of the video’s quality. You will receive a detailed depiction of the quality of a video, which may include evaluations and comments on various attributes of the video quality.
You may design questions through single-answer choices or open-ended responses.
When evaluating quality scoring tasks, we choose a relatively simple system prompts design to guarantee that the evaluation process is end-to-end (without any additional temporal information that may need manual extraction, like length, frame rate, and stalling information).
Video Quality Assessment
This section outlines classical video quality assessment tasks, differentiating between UGC and streaming video quality scoring. It describes the reliance on subjective experiments and the limitations of existing models in understanding video quality attributes.
Evaluation Examples on Q-bench-Video
This section presents evaluation examples from the Q-bench-Video dataset, illustrating the performance of the VQA 2 series models in assessing video quality through various question-answer scenarios.
Figures Explained
Frequently Asked Questions
The advent and proliferation of large multi-modal models (LMMs) have introduced new paradigms to computer vision and transformed various tasks into a unified visual question answering framework. The invention and rise of large multi-modal models (LMMs) have entered the domain of computer.
The paradigm of visual-language instruction tuning using multi-modal instruction datasets has markedly enhanced the performance of video LMMs in high-level visual tasks intimately related to video semantics such as video understanding and video temporal analysis. Co-Instruct has significantly improved large models’ ability.
Each dimension in all the radar charts is normalized using different ranges based on the specific data characteristics to highlight performance differences. Incorporating visual question answering to aid model training and inference has tremendous potential for development in low-level vision.
If such a depiction can not be found, it may be substituted with extended conversations, such as using GPT to rewrite and refine the annotated overall quality depictions, in-context depictions, and extended conversations. The primary presence of such distortion leads to a.
For example, q-align attains high precision and generalizability in video quality scoring. This limitation slightly impacts the diversity of model outputs and the accuracy of responses to specific questions.
This paper discusses a new approach to assessing video quality using advanced models that can answer questions about video content. It introduces a dataset specifically designed for this purpose and shows how these models can provide better insights into video quality.