Papers › Dual-modality seq2seq network for audio-visual event localization

Dual-modality seq2seq network for audio-visual event localization

20 Feb 2019arXiv:1902.07473archive 2025-07-28

Yan-Bo Lin, Yu-Jhe Li, Yu-Chiang Frank Wang

Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level). To address this task, we pro-pose a deep neural network named Audio-Visual sequence-to-sequence dual network (AVSDN). By jointly taking bothaudio and visual features at each time segment as inputs, ourproposed model learns global and local event information ina sequence to sequence manner, which can be realized in ei-ther fully supervised or weakly supervised settings. Empiricalresults confirm that our proposed method performs favorablyagainst recent deep learning approaches in both settings.

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YapengTian/AVE-ECCV18 mentioned on GitHubpytorch report
jasongief/cpsp mentioned on GitHubpytorch report

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1ran · honoured contract
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compute_accuracy_supervised jasongief/cpsp/cpsp_avel/weakly_supv_main.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 2c6b1e38dd0aa68a · report
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eucl_dist_output_shape identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · cc8acac4816ccb95 · report
step_decay identical code first harvested elsewhere ran · honoured contract fingerprinted licence of this copy not recorded · f604bf5b94c48316 · report
val identical code first harvested elsewhere unverified licence of this copy not recorded · 219c7611d3659ea1 · report

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audio-visual event localization

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