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Video Retrieval On Fivr 200K

Metrics

mAP (CSVR)
mAP (DSVR)
mAP (ISVR)

Results

Performance results of various models on this benchmark

Model Name
mAP (CSVR)
mAP (DSVR)
mAP (ISVR)
Paper TitleRepository
ViSiLf0.7970.8430.660ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning-
TCAc0.5530.570 0.473Temporal Context Aggregation for Video Retrieval with Contrastive Learning-
DnS (S^f_B)0.8630.9090.729DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval-
ViSiLsym0.7920.8330.654ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning-
VRAG (CS)0.6780.7230.554VRAG: Region Attention Graphs for Content-Based Video Retrieval-
ViSiLv (pt)0.8540.8990.723ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning-
DnS (S^c)0.558 0.574 0.476DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval-
S2VS0.8790.9270.746Self-Supervised Video Similarity Learning-
Jo et al. (SCFV+TNIP)0.8330.8960.674Exploring the Temporal Cues to Enhance Video Retrieval on Standardized CDVA
TCAf0.830 0.8770.703Temporal Context Aggregation for Video Retrieval with Contrastive Learning-
TCAsym0.6980.7280.592Temporal Context Aggregation for Video Retrieval with Contrastive Learning-
ViSiLv (tf)0.841 0.8920.702ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning-
VVS0.6890.711 0.590VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression-
S2VS0.8780.9250.739Self-Supervised Video Similarity Learning-
DnS (S^f_A)0..8750.921 0.741DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval-
VRAG (video)0.4700.4840.399VRAG: Region Attention Graphs for Content-Based Video Retrieval-
LAMV0.4660.4960.371LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers
0 of 17 row(s) selected.
Video Retrieval On Fivr 200K | SOTA | HyperAI