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Found 14 papers, 1 papers with code
PromptGen: Automatically Generate Prompts using Generative Models
Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt.
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation
Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains.
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets.
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation.
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models
In particular, we focus on few-shot image recognition tasks on pretrained vision-language models (PVLMs) and develop a method of prompting through prototype (PTP), where we define $K$ image prototypes and $K$ prompt prototypes.
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing
Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels.
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning
In this paper, we formulate the APP-installation user embedding learning into a bipartite graph embedding problem.
Boost CTR Prediction for New Advertisements via Modeling Visual Content
To overcome the limitations of behavior ID features in modeling new ads, we exploit the visual content in ads to boost the performance of CTR prediction models.
Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising
How to pair the video ads with the user search is the core task of Baidu video advertising.
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval
Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.
Cross-Probe BERT for Efficient and Effective Cross-Modal Search
Inspired by the great success of BERT in NLP tasks, many text-vision BERT models emerged recently.
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning
Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Recent neural network models have significantly advanced the task of coreference resolution.
Ranked #14 on
Coreference Resolution
on CoNLL 2012
