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Zeroshot Video Question Answer On Tgif Qa

Metrics

Accuracy

Confidence Score

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Confidence Score	Paper Title	Repository
Elysium	66.6	3.6	Elysium: Exploring Object-level Perception in Videos via MLLM
MiniGPT4-video-7B	72.22	-	MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
TS-LLaVA-34B	81.0	4.2	TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models
Video Chat-7B	34.4	2.3	VideoChat: Chat-Centric Video Understanding
SlowFast-LLaVA-34B	80.6	4.3	SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Video-LLaVA-7B	70.0	4.0	Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
VideoGPT+	74.6	4.1	VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
LinVT-Qwen2-VL (7B)	81.3	4.3	LinVT: Empower Your Image-level Large Language Model to Understand Videos
IG-VLM	79.1	4.2	An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
Video-ChatGPT-7B	51.4	3.0	Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
PLLaVA	80.6	4.3	PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Chat-UniVi-7B	69.0	3.8	Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
FrozenBiLM	41.9	-	Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Tarsier (34B)	82.5	4.4	Tarsier: Recipes for Training and Evaluating Large Video Description Models

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