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Dense Video Captioning On Activitynet

Metrics

BLEU-3

BLEU-4

METEOR

Results

Performance results of various models on this benchmark

				Paper Title	Repository
Vid2Seq	-	-	17	Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
ADV-INF + Global	-	9.45	16.36	Global Object Proposals for Improving Multi-Sentence Video Descriptions	-
Bi-directional+intra captioning	-	-	11.28	Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning	-
GVL	-	-	10.03	Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos
TSRM-CMG-HRNN+SCST	-	-	9.71	Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
PDVC (TSP features, no SCST)	-	2.17	9.03	End-to-End Dense Video Captioning with Parallel Decoding
TSP	4.16	2.02	8.75	TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
CM²	-	-	8.55	Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval
BMT	3.84	1.88	8.44	A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
iPerceive (Chadha et al., 2020)	2.93	1.29	7.87	iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering	-
MDVC	2.6	1.07	7.31	Multi-modal Dense Video Captioning
VTimeLLM	-	-	-	VTimeLLM: Empower LLM to Grasp Video Moments

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Dense Video Captioning On Activitynet | SOTA | HyperAI