Search Results for author:
Found 7 papers, 5 papers with code
NoteLLM: A Retrievable Large Language Model for Note Recommendation
Indeed, learning to generate hashtags/categories can potentially enhance note embeddings, both of which compress key note information into limited content.
Communication-Efficient Distributed Learning with Local Immediate Error Compensation
However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate.
Multi-Grained Multimodal Interaction Network for Entity Linking
Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions.
A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference
In this paper, we present a solution for enhancing video alignment to improve multi-step inference.
AU-aware graph convolutional network for Macro- and Micro-expression spotting
Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs.
Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach
Affordance-centric Question-driven Task Completion for Egocentric Assistant(AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step.
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.
