Papers with feature alignment
SA-DETR:Span Aware Detection Transformer for Moment Retrieval (2025.coling-main)
Copied to clipboard
| Challenge: | Moment Retrieval aims to locate video segments related to text. |
| Approach: | They propose a method that leverages the importance of instance related span anchors . they initialize span anchor using instance related fuse token and supervise them with GT labels . |
| Outcome: | The proposed method achieves competitive results on QVHighlights, Charades-STA and TACoS. |
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation. |
| Approach: | They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports. |
| Outcome: | The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks. |
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues (2025.acl-long)
Copied to clipboard
| Challenge: | linguistic research shows that non-verbal cues, such as gestures, play a crucial role in spoken discourse. |
| Approach: | They propose to integrate gestures into language models by embedding human motion sequences into discrete gesture tokens and aligning them with text embeddables. |
| Outcome: | The proposed model improves on spoken discourse, the authors show . the study aims to improve the accuracy of discourse markers and quantifiers . |
JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal emotion and intent recognition tasks focus on classification, not rationale and intrinsic connections between these states. |
| Approach: | They propose a task that requires models to jointly predict emotion and intent while generating natural language explanations for why they co-occur. |
| Outcome: | The proposed model outperforms baseline models in prediction and explanation generation. |