Search Results for author:
Found 11 papers, 5 papers with code
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
TRACE: Temporal Grounding Video LLM via Causal Event Modeling
To effectively handle various tasks simultaneously and enable zero-shot prediction, there is a growing trend in employing video LLMs for VTG tasks.
Enhancing Long Video Understanding via Hierarchical Event-Based Memory
While this method excels in short video understanding, it may result in a blend of multiple event information in long videos due to coarse compression, which causes information redundancy.
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications.
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material.
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments.
VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video browsing and editing.
Can We Edit Multimodal Large Language Models?
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
Knowledge-augmented Few-shot Visual Relation Detection
Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.
Lifelong Intent Detection via Multi-Strategy Rebalancing
In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge.
