Temporal Tessellation: A Unified Approach for Video Analysis

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VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (2021, neutral): “Accuracy ↑ S UPERVISED LSTM-fusion (Yu et al., 2018) 38.3 C+LSTM+SA-FC7 (Torabi et al., 2016) 60.2 SNUVL (Yu et al., 2016) 65.4 EITanque (Kaufman et al., 2017) 65.5 CT-SAN (Yu et al., 2017) 66.4 VSE-LSTM (Kiros et al., 2014) 67.3 MLB (Kim et al., 2016) 76.1 JSFusion(Yu et al., 2018) 83.4…”
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval (2021, methodology): “The pixel-level approach trains the model with raw video as input directly (Torabi et al., 2016; Kiros et al., 2014; Yu et al., 2016a; Kaufman et al., 2017; Yu et al., 2017, 2018).”
Excitation Backprop for RNNs (2017, unknown): “[9] 61.”
ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING (2018, methodology): “Much like conventional encoder-decoder methods for video captioning (Venugopalan et al. (2014); Donahue et al. (2015); Kaufman et al. (2016) and Sutskever et al. (2014)), our decoder generates captions using a softmax over vocabulary words, conditioned on previously generated words. loss is the…”