Search Results for author:
Found 26 papers, 6 papers with code
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training
Additionally, a novel attribute-promoted image reconstruction task is proposed, which further enhances the fine-grained ability of the model by leveraging the representative attributes from the image modality.
MADiff: Text-Guided Fashion Image Editing with Mask Prediction and Attention-Enhanced Diffusion
Specifically, to more accurately identify editing region, the MaskNet is proposed, in which the foreground region, densepose and mask prompts from large language model are fed into a lightweight UNet to predict the mask for editing region.
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
We argue that this limitation is closely linked to the models' visual grounding capabilities.
Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples
Existing Vision-Language Pretraining (VLP) methods have achieved remarkable improvements across a variety of vision-language tasks, confirming their effectiveness in capturing coarse-grained semantic correlations.
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
Here, we propose MoDULA (\textbf{M}ixture \textbf{o}f \textbf{D}omain-Specific and \textbf{U}niversal \textbf{L}oR\textbf{A}), a novel \textbf{P}arameter \textbf{E}fficient \textbf{F}ine-\textbf{T}uning (PEFT) \textbf{M}ixture-\textbf{o}f-\textbf{E}xpert (MoE) paradigm for improved fine-tuning and parameter efficiency in multi-task learning.
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
In this paper, we propose a Multi-domain Low-Rank Adaptive network (MLoRA) for CTR prediction, where we introduce a specialized LoRA module for each domain.
General2Specialized LLMs Translation for E-commerce
Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and legal documents.
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
Ranked #1 on
Camouflaged Object Segmentation
on COD
(using extra training data)
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+3
EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising
We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising.
Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval
Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images.
QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning
Daily images may convey abstract meanings that require us to memorize and infer profound information from them.
Masked Vision-Language Transformer in Fashion
We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation.
Fast Heterogeneous Federated Learning with Hybrid Client Selection
We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction.
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection
However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL.
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers.
Network Clustering for Multi-task Learning
The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.
Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
Deep Hierarchical Classification for Category Prediction in E-commerce System
In e-commerce system, category prediction is to automatically predict categories of given texts.
