Search Results for author: Jianan Wang

Found 26 papers, 10 papers with code

Implicit Sentiment Analysis with Event-centered Text Representation

no code implementations EMNLP 2021 Deyu Zhou, Jianan Wang, Linhai Zhang, Yulan He

Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.

Representation Learning Sentiment Analysis

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

no code implementations18 Oct 2023 Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li, Lei Zhang, Jian Zhang, Li Yuan

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies.

Text to 3D

Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices

no code implementations5 Sep 2023 Bojia Zi, Xianbiao Qi, Lingzhi Wang, Jianan Wang, Kam-Fai Wong, Lei Zhang

In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs).

DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation

no code implementations21 Jun 2023 Yukun Huang, Jianan Wang, Yukai Shi, Xianbiao Qi, Zheng-Jun Zha, Lei Zhang

Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation.

Image Generation Text to 3D

Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant

no code implementations15 Jun 2023 Xianbiao Qi, Jianan Wang, Lei Zhang

This article provides a comprehensive understanding of optimization in deep learning, with a primary focus on the challenges of gradient vanishing and gradient exploding, which normally lead to diminished model representational ability and training instability, respectively.

detrex: Benchmarking Detection Transformers

1 code implementation12 Jun 2023 Tianhe Ren, Shilong Liu, Feng Li, Hao Zhang, Ailing Zeng, Jie Yang, Xingyu Liao, Ding Jia, Hongyang Li, He Cao, Jianan Wang, Zhaoyang Zeng, Xianbiao Qi, Yuhui Yuan, Jianwei Yang, Lei Zhang

To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.

Benchmarking object-detection +2

LipsFormer: Introducing Lipschitz Continuity to Vision Transformers

1 code implementation19 Apr 2023 Xianbiao Qi, Jianan Wang, Yihao Chen, Yukai Shi, Lei Zhang

In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability.

DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training

1 code implementation CVPR 2023 Yihao Chen, Xianbiao Qi, Jianan Wang, Lei Zhang

In this way, we can reduce the GPU memory consumption of contrastive loss computation from $\bigO(B^2)$ to $\bigO(\frac{B^2}{N})$, where $B$ and $N$ are the batch size and the number of GPUs used for training.

Contrastive Learning

HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation

1 code implementation ICCV 2023 Xuan Ju, Ailing Zeng, Chenchen Zhao, Jianan Wang, Lei Zhang, Qiang Xu

While such a plug-and-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which essentially conducts image feature editing for condition enforcement.

Denoising Image Generation

Entity-Level Text-Guided Image Manipulation

1 code implementation22 Feb 2023 Yikai Wang, Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Wei zhang, Yanwei Fu

In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.

Denoising Image Manipulation

Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning

no code implementations1 Jan 2023 Lu Jiang, Yibin Wang, Jianan Wang, Pengyang Wang, Minghao Yin

To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation.

Graph Representation Learning

A Multi-Source Information Learning Framework for Airbnb Price Prediction

no code implementations1 Jan 2023 Lu Jiang, Yuanhan Li, Na Luo, Jianan Wang, Qiao Ning

Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding.

Exploring Vision Transformers as Diffusion Learners

no code implementations28 Dec 2022 He Cao, Jianan Wang, Tianhe Ren, Xianbiao Qi, Yihao Chen, Yuan YAO, Lei Zhang

We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND).

Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning

no code implementations24 Dec 2022 Yanan Xiao, Minyu Liu, Zichen Zhang, Lu Jiang, Minghao Yin, Jianan Wang

We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network.

reinforcement-learning Reinforcement Learning (RL) +2

ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

no code implementations CVPR 2022 Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Chunjing Xu, Yanwei Fu

Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application.

Image Generation Image Manipulation

Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions

1 code implementation15 Nov 2020 Jianan Wang, Boyang Li, Xiangyu Fan, Jing Lin, Yanwei Fu

The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays.

A Combinatorial Perspective on Transfer Learning

1 code implementation NeurIPS 2020 Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness

Our main postulate is that the combination of task segmentation, modular learning and memory-based ensembling can give rise to generalization on an exponentially growing number of unseen tasks.

Continual Learning Transfer Learning

Online Learning in Contextual Bandits using Gated Linear Networks

no code implementations NeurIPS 2020 Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness

We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB).

Multi-Armed Bandits

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

1 code implementation NeurIPS 2019 Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni

The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.

Ranked #13 on 3D Instance Segmentation on S3DIS (mPrec metric)

3D Instance Segmentation Clustering +2

Group Linguistic Bias Aware Neural Response Generation

no code implementations WS 2017 Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, Baoxun Wang

For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc.

Response Generation

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