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Zero-Shot Video Question Answer
Zeroshot Video Question Answer On Activitynet
Zeroshot Video Question Answer On Activitynet
Metrics
Accuracy
Confidence Score
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Confidence Score
Paper Title
Repository
MovieChat
45.7
3.1
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
-
BT-Adapter (zero-shot)
46.1
3.2
BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
-
Tarsier (34B)
61.6
3.7
Tarsier: Recipes for Training and Evaluating Large Video Description Models
-
VideoChat2
49.1
3.3
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
-
Chat-UniVi
46.1
3.3
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
-
LLaMA-VID-13B (2 Token)
47.5
3.3
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
-
PLLaVA (34B)
60.9
3.7
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
-
IG-VLM
58.4
3.5
An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
-
SlowFast-LLaVA-34B
59.2
3.5
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
-
LLaVA-Mini
53.5
3.5
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
-
FrozenBiLM
24.7
-
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
-
Video Chat
26.5
2.2
VideoChat: Chat-Centric Video Understanding
-
LLaMA-VID-7B (2 Token)
47.4
3.3
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
-
Video-ChatGPT
35.2
2.7
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
-
Flash-VStream
51.9
3.4
Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams
-
Video-LLaVA
45.3
3.3
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
-
TS-LLaVA-34B
58.9
3.5
TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models
-
Elysium
43.4
2.9
Elysium: Exploring Object-level Perception in Videos via MLLM
-
PPLLaVA-7B
60.7
3.6
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
-
LinVT-Qwen2-VL(7B)
60.1
3.6
LinVT: Empower Your Image-level Large Language Model to Understand Videos
-
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