Papers › Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video

Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video

4 Apr 2022ICCV 2021 10arXiv:2204.01265archive 2025-07-28

Minsu Kim, Joanna Hong, Se Jin Park, Yong Man Ro

In this paper, we introduce a novel audio-visual multi-modal bridging framework that can utilize both audio and visual information, even with uni-modal inputs. We exploit a memory network that stores source (i.e., visual) and target (i.e., audio) modal representations, where source modal representation is what we are given, and target modal representations are what we want to obtain from the memory network. We then construct an associative bridge between source and target memories that considers the interrelationship between the two memories. By learning the interrelationship through the associative bridge, the proposed bridging framework is able to obtain the target modal representations inside the memory network, even with the source modal input only, and it provides rich information for its downstream tasks. We apply the proposed framework to two tasks: lip reading and speech reconstruction from silent video. Through the proposed associative bridge and modality-specific memories, each task knowledge is enriched with the recalled audio context, achieving state-of-the-art performance. We also verify that the associative bridge properly relates the source and target memories.

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Tasks

Lip Reading

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Lipreading CAS-VSR-W1k (LRW-1000) 3D Conv + ResNet-18 + Bi-GRU + Visual-Audio Memory Top-1 Accuracy 50.82% #4 of 9 Archive leaderboard report
Lipreading Lip Reading in the Wild 3D Conv + ResNet-18 + Bi-GRU + Visual-Audio Memory Top-1 Accuracy 85.4 #11 of 22 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Memory Network

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