In recent years, significant progress has been made in video instance
se...
Sketch-based shape modeling aims to bridge the gap between 2D drawing an...
Multi-modal keyphrase generation aims to produce a set of keyphrases tha...
Self-supervised masked image modeling has shown promising results on nat...
Multi-choice questions (MCQs) serve as a common yet important task forma...
Semantic segmentation in autonomous driving has been undergoing an evolu...
Large Language Models (LLMs) present strong general capabilities, and a
...
Large language models (LLMs) are capable of performing conditional seque...
Autonomous agents empowered by Large Language Models (LLMs) have undergo...
Catastrophic forgetting (CF) is a phenomenon that occurs in machine lear...
N-gram matching-based evaluation metrics, such as BLEU and chrF, are wid...
EduChat (https://www.educhat.top/) is a large-scale language model
(LLM)...
3D human pose estimation in outdoor environments has garnered increasing...
Despite the advancements of open-source large language models (LLMs) and...
As large language models (LLMs) generate texts with increasing fluency a...
With the overwhelming trend of mask image modeling led by MAE, generativ...
Open-sourced large language models (LLMs) have demonstrated remarkable
e...
The partial linear Cox model for interval-censoring is well-studied unde...
Multilingual pre-trained language models have demonstrated impressive
(z...
In this paper, we propose an accurate data-free post-training quantizati...
This work examines the presence of modularity in pre-trained Transformer...
Injecting external knowledge can improve the performance of pre-trained
...
Parameter-efficient tuning methods (PETs) have achieved promising result...
In-context learning (ICL) emerges as a promising capability of large lan...
Pretrained language models have achieved remarkable success in various
n...
Many-to-many multimodal summarization (M^3S) task aims to generate
summa...
Existing models for named entity recognition (NER) are mainly based on
l...
Task-incremental continual learning refers to continually training a mod...
Currently, density-based clustering algorithms are widely applied becaus...
Large language models (LLMs) have notably accelerated progress towards
a...
Comprehending characters' personalities is a crucial aspect of story rea...
To adapt text summarization to the multilingual world, previous work pro...
Continual pre-training is the paradigm where pre-trained language models...
Multilingual vision-language (V L) pre-training has achieved remarkabl...
Long-form question answering (LFQA) aims at answering complex, open-ende...
In this paper, we introduce WeLayout, a novel system for segmenting the
...
Representation forgetting refers to the drift of contextualized
represen...
Pre-trained Language Models (PLMs) may be poisonous with backdoors or bi...
Recently, a series of pioneer studies have shown the potency of pre-trai...
In this paper, we propose a new detection framework for 3D small object
...
Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000
l...
Existing neural machine translation (NMT) studies mainly focus on develo...
In this paper, we propose an ultrafast automated model compression frame...
Cross-domain recommendation (CDR) aims to leverage the users' behaviors ...
In this paper, we propose binary sparse convolutional networks called BS...
Efficiently digitizing high-fidelity animatable human avatars from video...
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on...
3D scene understanding plays a vital role in vision-based autonomous dri...
Most facial landmark detection methods predict landmarks by mapping the ...
Recently, the emergence of ChatGPT has attracted wide attention from the...