Challenge: Existing methods for paragraph captioning videos without event ground truths generate one sentence for each event, but without event labels, it is difficult to locate the transitions between events and minimize repetition.
Approach: They propose a module that dynamically groups event information with the help of action information for the entire video and excludes redundant frames within pre-defined clips.
Outcome: The proposed module outperforms the state-of-the-art methods on all metrics.

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Challenge: Recent work adopts a "pre-training + fine-tuning" approach for zero-shot transfer to end tasks without fine- tuning.
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Intent Discovery with Frame-guided Semantic Regularization and Augmentation (2023.findings-acl)

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Challenge: Existing intent discovery methods focus on transferring prior knowledge of known intents to unknown ones.
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Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)

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Challenge: Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work .
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FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)

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Challenge: Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data.
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Hierarchical Context-aware Network for Dense Video Event Captioning (2021.acl-long)

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Challenge: Existing technologies for dense video event captioning generate fine-grained captions for all events in a long untrimmed video.
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Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)

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Challenge: Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations.
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A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video (2023.emnlp-main)

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Challenge: Existing methods to summarize video content have only considered video and image data, and the trend towards multimodal video summarization is changing.
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RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval (2022.findings-emnlp)

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Challenge: sparse sampling of videos suffers from inter-modal redundancy and visual redundancies . et al., 2021) proposes to sparsestly sample frames from videos to alleviate temporal redundance .
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See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval (2026.findings-eacl)

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Challenge: Existing video moment retrieval methods rely on sparse frame sampling, risking information loss.
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Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)

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Challenge: Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele .
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