56 citations · 186 across the 20 of their papers we have counts for
20 papers
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval
Pandeng Li, Hongtao Xie, Jiannan Ge +3
Unsupervised video hashing usually optimizes binary codes by learning to reconstruct input videos. Such reconstruction constraint spends much effort on frame-level temporal context…
Balanced Classification: A Unified Framework for Long-Tailed Object Detection
Tianhao Qi, Hongtao Xie, Pandeng Li +2
Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. In this paper, we c…
MomentDiff: Generative Video Moment Retrieval from Random to Real
Pandeng Li, Chen-Wei Xie, Hongtao Xie +5
Video moment retrieval pursues an efficient and generalized solution to identify the specific temporal segments within an untrimmed video that correspond to a given language descri…
DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation
Zhuowei Chen, Shancheng Fang, Wei Liu +4
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for con…
Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization
Huan Ren, Wenfei Yang, Tianzhu Zhang +1
Weakly-supervised temporal action localization aims to localize and recognize actions in untrimmed videos with only video-level category labels during training. Without instance-le…
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation
Mengqi Huang, Zhendong Mao, Quan Wang +1
Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image gener…
Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization
Mengqi Huang, Zhendong Mao, Zhuowei Chen +1
Existing vector quantization (VQ) based autoregressive models follow a two-stage generation paradigm that first learns a codebook to encode images as discrete codes, and then compl…
Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition
Boqiang Zhang, Hongtao Xie, Yuxin Wang +2
Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task. However, due to lacking the perception of linguistic know…
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
Benfeng Xu, An Yang, Junyang Lin +4
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to…
De Novo Molecular Generation via Connection-aware Motif Mining
Zijie Geng, Shufang Xie, Yingce Xia +6
De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibi…
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
Jie Wang, Rui Yang, Zijie Geng +7
Generalization in partially observed markov decision processes (POMDPs) is critical for successful applications of visual reinforcement learning (VRL) in real scenarios. A widely u…
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Zhihai Wang, Xijun Li, Jie Wang +5
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection -- which aim…
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
Shancheng Fang, Zhendong Mao, Hongtao Xie +3
Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for ch…
Exploring Stroke-Level Modifications for Scene Text Editing
Yadong Qu, Qingfeng Tan, Hongtao Xie +3
Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures…
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
Zheren Fu, Zhendong Mao, Bo Hu +2
Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and info…
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
Jiahao Li, Quan Wang, Zhendong Mao +3
Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarit…
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
Zhiying Lu, Hongtao Xie, Chuanbin Liu +1
There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is co…
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation
Mengqi Huang, Zhendong Mao, Penghui Wang +2
Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stac…
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer
Quanwei Yang, Xinchen Liu, Wu Liu +4
Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person. Existing methods for HVMT main…
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
Xiaodong Chen, Wu Liu, Xinchen Liu +3
Recognizing human actions from point cloud videos has attracted tremendous attention from both academia and industry due to its wide applications like automatic driving, robotics,…