Search Results for author:
Found 10 papers, 5 papers with code
RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation
Moreover, to mitigate false pruning in the process of constrained evidence generation, we introduce (1) hierarchical FM-Index constraints, which generate corpus-constrained clues to identify a subset of relevant documents before evidence generation, reducing irrelevant decoding space; and (2) a forward-looking constrained decoding strategy, which considers the relevance of future sequences to improve evidence accuracy.
HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning
However, the structure encoder has scale problem.
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge
To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding.
Plug-and-Play Document Modules for Pre-trained Models
By inserting document plugins into the backbone PTM for downstream tasks, we can encode a document one time to handle multiple tasks, which is more efficient than conventional encoding-task coupling methods that simultaneously encode documents and input queries using task-specific encoders.
Rethinking Dense Retrieval's Few-Shot Ability
Moreover, the dataset is disjointed into base and novel classes, allowing DR models to be continuously trained on ample data from base classes and a few samples in novel classes.
FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training
We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics.
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training
In this paper, we propose a fine-grained VLP scheme without object annotations from the linguistic perspective.
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
We convert a lightweight FCOSR model to TensorRT format, which achieves 73. 93 mAP on DOTA1. 0 at a speed of 10. 68 FPS on Jetson Xavier NX with single scale.
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval
Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE).
MarlRank: Multi-agent Reinforced Learning to Rank
By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.
