Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations. In addition, we introduce Adaptive Weighting scheme for our contrastive learning approach in order to increase flexibility in optimization. Lastly, we propose Multimodal Interaction module to address the unalignment nature of multimodal data, thereby assisting the model in producing more reasonable multimodal representations. Experimental results show that our method outperforms prior baselines and achieves state-of-the-art results on two publicly available benchmark datasets for MRHP problem.
verdicts
UNVERDICTED 4representative citing papers
A multi-scale and cross-scale contrastive learning framework uses intra-encoder stage features and a new sampling process to link short-range and long-range video moments for temporal grounding.
Introduces listwise attention, listwise loss, and GBDT predictor to improve multimodal review helpfulness ranking over prior FCNN and pairwise approaches.
DemaFormer pairs energy-based modeling with a damped-EMA Transformer to localize video moments matching language queries and reports gains over baselines on four datasets.
citing papers explorer
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Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.
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Multi-Scale Contrastive Learning for Video Temporal Grounding
A multi-scale and cross-scale contrastive learning framework uses intra-encoder stage features and a new sampling process to link short-range and long-range video moments for temporal grounding.
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Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Introduces listwise attention, listwise loss, and GBDT predictor to improve multimodal review helpfulness ranking over prior FCNN and pairwise approaches.
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DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding
DemaFormer pairs energy-based modeling with a damped-EMA Transformer to localize video moments matching language queries and reports gains over baselines on four datasets.