Search Results for author: Yingfei Sun

Found 18 papers, 8 papers with code

Described Spatial-Temporal Video Detection

no code implementations8 Jul 2024 Wei Ji, Xiangyan Liu, Yingfei Sun, Jiajun Deng, You Qin, Ammar Nuwanna, Mengyao Qiu, Lina Wei, Roger Zimmermann

However, in the video domain, the existing setting, i. e., spatial-temporal video grounding (STVG), is formulated to only detect one pre-existing object in each frame, ignoring the fact that language descriptions can involve none or multiple entities within a video.

Multi-class Classification Temporal Localization +1

Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

no code implementations2 Jul 2024 Ke Ma, Qianqian Xu, Jinshan Zeng, Wei Liu, Xiaochun Cao, Yingfei Sun, Qingming Huang

Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution.


Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering

1 code implementation16 Apr 2024 Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, Yingfei Sun

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.

Information Retrieval Language Modelling +3

Text Data-Centric Image Captioning with Interactive Prompts

no code implementations28 Mar 2024 Yiyu Wang, Hao Luo, Jungang Xu, Yingfei Sun, Fan Wang

Among them, the mainstream solution is to project image embeddings into the text embedding space with the assistance of consistent representations between image-text pairs from the CLIP model.

Image Captioning

Academically intelligent LLMs are not necessarily socially intelligent

1 code implementation11 Mar 2024 Ruoxi Xu, Hongyu Lin, Xianpei Han, Le Sun, Yingfei Sun

The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear.

Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection

1 code implementation1 Feb 2024 Xinlin Peng, Ying Zhou, Ben He, Le Sun, Yingfei Sun

This paper aims to bridge this gap by constructing AIG-ASAP, an AI-generated student essay dataset, employing a range of text perturbation methods that are expected to generate high-quality essays while evading detection.

Sentence Text Generation

AI for social science and social science of AI: A Survey

no code implementations22 Jan 2024 Ruoxi Xu, Yingfei Sun, Mengjie Ren, Shiguang Guo, Ruotong Pan, Hongyu Lin, Le Sun, Xianpei Han

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities.

Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection

no code implementations31 Jul 2023 Xuanang Chen, Ben He, Le Sun, Yingfei Sun

Neural ranking models (NRMs) have undergone significant development and have become integral components of information retrieval (IR) systems.

Adversarial Attack Information Retrieval +2

Understanding Differential Search Index for Text Retrieval

1 code implementation3 May 2023 Xiaoyang Chen, Yanjiang Liu, Ben He, Le Sun, Yingfei Sun

The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query.

Information Retrieval Text Retrieval

Towards Imperceptible Document Manipulations against Neural Ranking Models

no code implementations3 May 2023 Xuanang Chen, Ben He, Zheng Ye, Le Sun, Yingfei Sun

Additionally, current methods rely heavily on the use of a well-imitated surrogate NRM to guarantee the attack effect, which makes them difficult to use in practice.

Adversarial Text Language Modelling +1

End-to-End Transformer Based Model for Image Captioning

2 code implementations29 Mar 2022 Yiyu Wang, Jungang Xu, Yingfei Sun

Firstly, we adopt SwinTransformer to replace Faster R-CNN as the backbone encoder to extract grid-level features from given images; Then, referring to Transformer, we build a refining encoder and a decoder.

Decoder Image Captioning

Simplified TinyBERT: Knowledge Distillation for Document Retrieval

4 code implementations16 Sep 2020 Xuanang Chen, Ben He, Kai Hui, Le Sun, Yingfei Sun

Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.

Document Ranking Knowledge Distillation +1

PARADE: Passage Representation Aggregation for Document Reranking

1 code implementation20 Aug 2020 Canjia Li, Andrew Yates, Sean MacAvaney, Ben He, Yingfei Sun

In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.

Document Ranking Knowledge Distillation

A Survey on Neural Machine Reading Comprehension

no code implementations10 Jun 2019 Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun

Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge.

Machine Reading Comprehension

Image Captioning based on Deep Learning Methods: A Survey

no code implementations20 May 2019 Yiyu Wang, Jungang Xu, Yingfei Sun, Ben He

Image captioning is a challenging task and attracting more and more attention in the field of Artificial Intelligence, and which can be applied to efficient image retrieval, intelligent blind guidance and human-computer interaction, etc.

Decoder Image Captioning +2

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

1 code implementation EMNLP 2018 Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, Jungang Xu

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.

Ad-Hoc Information Retrieval Information Retrieval +1

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