Search Results for author: Zicheng Liu

Found 123 papers, 60 papers with code

Switch EMA: A Free Lunch for Better Flatness and Sharpness

2 code implementations14 Feb 2024 Siyuan Li, Zicheng Liu, Juanxi Tian, Ge Wang, Zedong Wang, Weiyang Jin, Di wu, Cheng Tan, Tao Lin, Yang Liu, Baigui Sun, Stan Z. Li

Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization.

Attribute Image Classification +7

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

1 code implementation13 Feb 2024 Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan Z. Li

Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.

Drug Discovery

StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis

no code implementations30 Jan 2024 Zecheng Tang, Chenfei Wu, Zekai Zhang, Mingheng Ni, Shengming Yin, Yu Liu, Zhengyuan Yang, Lijuan Wang, Zicheng Liu, Juntao Li, Nan Duan

To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes.

Vector Graphics

A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization

no code implementations19 Jan 2024 Jiazhao Zhang, Ying Hung, Chung-Ching Lin, Zicheng Liu

To capture the conditional dependence between branching and nested parameters, a unified Bayesian optimization framework is proposed.

Bayesian Optimization Hyperparameter Optimization

Bring Metric Functions into Diffusion Models

no code implementations4 Jan 2024 Jie An, Zhengyuan Yang, JianFeng Wang, Linjie Li, Zicheng Liu, Lijuan Wang, Jiebo Luo

The first module, similar to a standard DDPM, learns to predict the added noise and is unaffected by the metric function.

Denoising

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond

1 code implementation31 Dec 2023 Siyuan Li, Luyuan Zhang, Zedong Wang, Di wu, Lirong Wu, Zicheng Liu, Jun Xia, Cheng Tan, Yang Liu, Baigui Sun, Stan Z. Li

As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data.

Representation Learning Self-Supervised Learning

Segment and Caption Anything

1 code implementation1 Dec 2023 Xiaoke Huang, JianFeng Wang, Yansong Tang, Zheng Zhang, Han Hu, Jiwen Lu, Lijuan Wang, Zicheng Liu

We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions.

object-detection Object Detection +1

MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning

no code implementations29 Nov 2023 Chaoyi Zhang, Kevin Lin, Zhengyuan Yang, JianFeng Wang, Linjie Li, Chung-Ching Lin, Zicheng Liu, Lijuan Wang

We present MM-Narrator, a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD).

In-Context Learning Text Generation

MM-VID: Advancing Video Understanding with GPT-4V(ision)

no code implementations30 Oct 2023 Kevin Lin, Faisal Ahmed, Linjie Li, Chung-Ching Lin, Ehsan Azarnasab, Zhengyuan Yang, JianFeng Wang, Lin Liang, Zicheng Liu, Yumao Lu, Ce Liu, Lijuan Wang

We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding.

Video Understanding

On the Hidden Waves of Image

no code implementations19 Oct 2023 Yinpeng Chen, Dongdong Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

We term this phenomenon hidden waves, as it reveals that, although the speeds of the set of wave equations and autoregressive coefficient matrices are latent, they are both learnable and shared across images.

Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation

no code implementations12 Oct 2023 Zhengyuan Yang, JianFeng Wang, Linjie Li, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Lijuan Wang

We introduce ``Idea to Image,'' a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation.

OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation

no code implementations11 Oct 2023 Jie An, Zhengyuan Yang, Linjie Li, JianFeng Wang, Kevin Lin, Zicheng Liu, Lijuan Wang, Jiebo Luo

We hope our proposed framework, benchmark, and LMM evaluation could help establish the intriguing interleaved image-text generation task.

Question Answering Text Generation

Completing Visual Objects via Bridging Generation and Segmentation

no code implementations1 Oct 2023 Xiang Li, Yinpeng Chen, Chung-Ching Lin, Hao Chen, Kai Hu, Rita Singh, Bhiksha Raj, Lijuan Wang, Zicheng Liu

This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components.

Image Generation Object +1

The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

1 code implementation29 Sep 2023 Zhengyuan Yang, Linjie Li, Kevin Lin, JianFeng Wang, Chung-Ching Lin, Zicheng Liu, Lijuan Wang

We hope that this preliminary exploration will inspire future research on the next-generation multimodal task formulation, new ways to exploit and enhance LMMs to solve real-world problems, and gaining better understanding of multimodal foundation models.

ORES: Open-vocabulary Responsible Visual Synthesis

1 code implementation26 Aug 2023 Minheng Ni, Chenfei Wu, Xiaodong Wang, Shengming Yin, Lijuan Wang, Zicheng Liu, Nan Duan

In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content.

Image Generation Language Modelling

MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

1 code implementation4 Aug 2023 Weihao Yu, Zhengyuan Yang, Linjie Li, JianFeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, Lijuan Wang

Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking.

Math Zero-Shot Visual Question Answring

Does Full Waveform Inversion Benefit from Big Data?

no code implementations28 Jul 2023 Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang, Yinpeng Chen, Benjamin Consolvo, Zicheng Liu, Youzuo Lin

This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI).

Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models

no code implementations27 Jul 2023 Xin Yuan, Linjie Li, JianFeng Wang, Zhengyuan Yang, Kevin Lin, Zicheng Liu, Lijuan Wang

In this paper, we study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis.

Denoising

DisCo: Disentangled Control for Realistic Human Dance Generation

1 code implementation30 Jun 2023 Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang

In this paper, we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects, backgrounds, and poses; (ii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources seamlessly.

Attribute

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

2 code implementations NeurIPS 2023 Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z. Li

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner.

Weather Forecasting

RefineVIS: Video Instance Segmentation with Temporal Attention Refinement

no code implementations7 Jun 2023 Andre Abrantes, Jiang Wang, Peng Chu, Quanzeng You, Zicheng Liu

We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using sequence context.

Ranked #3 on Video Instance Segmentation on YouTube-VIS 2021 (using extra training data)

Contrastive Learning Denoising +4

Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance

no code implementations25 May 2023 Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive).

Image Classification Image Reconstruction +3

Conformal Inference for Invariant Risk Minimization

no code implementations22 May 2023 Wenlu Tang, Zicheng Liu

The application of machine learning models can be significantly impeded by the occurrence of distributional shifts, as the assumption of homogeneity between the population of training and testing samples in machine learning and statistics may not be feasible in practical situations.

Neural Voting Field for Camera-Space 3D Hand Pose Estimation

no code implementations CVPR 2023 Lin Huang, Chung-Ching Lin, Kevin Lin, Lin Liang, Lijuan Wang, Junsong Yuan, Zicheng Liu

We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation.

3D Hand Pose Estimation regression

Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning

no code implementations27 Apr 2023 Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin

Geophysics has witnessed success in applying deep learning to one of its core problems: full waveform inversion (FWI) to predict subsurface velocity maps from seismic data.

Geophysics Image-to-Image Translation +1

Adaptive Human Matting for Dynamic Videos

1 code implementation CVPR 2023 Chung-Ching Lin, Jiang Wang, Kun Luo, Kevin Lin, Linjie Li, Lijuan Wang, Zicheng Liu

The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications.

Image Matting Video Matting

Binary Latent Diffusion

no code implementations CVPR 2023 Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu

In this paper, we show that a binary latent space can be explored for compact yet expressive image representations.

Image Generation Quantization +1

Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

1 code implementation29 Mar 2023 Zihan Liu, Yun Luo, Lirong Wu, Zicheng Liu, Stan Z. Li

It has become cognitive inertia to employ cross-entropy loss function in classification related tasks.

Equivariant Similarity for Vision-Language Foundation Models

1 code implementation ICCV 2023 Tan Wang, Kevin Lin, Linjie Li, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang

Unlike the existing image-text similarity objective which only categorizes matched pairs as similar and unmatched pairs as dissimilar, equivariance also requires similarity to vary faithfully according to the semantic changes.

Retrieval Text Retrieval +2

Energy-Inspired Self-Supervised Pretraining for Vision Models

no code implementations2 Feb 2023 Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu

In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e. g., an extra decoder.

Colorization Denoising +2

Federated Learning for Inference at Anytime and Anywhere

no code implementations8 Dec 2022 Zicheng Liu, Da Li, Javier Fernandez-Marques, Stefanos Laskaridis, Yan Gao, Łukasz Dudziak, Stan Z. Li, Shell Xu Hu, Timothy Hospedales

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.

Federated Learning

GRiT: A Generative Region-to-text Transformer for Object Understanding

1 code implementation1 Dec 2022 Jialian Wu, JianFeng Wang, Zhengyuan Yang, Zhe Gan, Zicheng Liu, Junsong Yuan, Lijuan Wang

Specifically, GRiT consists of a visual encoder to extract image features, a foreground object extractor to localize objects, and a text decoder to generate open-set object descriptions.

Dense Captioning Descriptive +3

MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction

no code implementations24 Nov 2022 Kevin Lin, Chung-Ching Lin, Lin Liang, Zicheng Liu, Lijuan Wang

Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain.

3D Human Pose Estimation Hand Pose Estimation

ReCo: Region-Controlled Text-to-Image Generation

no code implementations CVPR 2023 Zhengyuan Yang, JianFeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang

Human evaluation on PaintSkill shows that ReCo is +19. 28% and +17. 21% more accurate in generating images with correct object count and spatial relationship than the T2I model.

Position

Self-Supervised Learning based on Heat Equation

no code implementations23 Nov 2022 Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

When transferring to object detection with frozen backbone, QB-Heat outperforms MoCo-v2 and supervised pre-training on ImageNet by 7. 9 and 4. 5 AP respectively.

Image Classification object-detection +2

Exploring Discrete Diffusion Models for Image Captioning

1 code implementation21 Nov 2022 Zixin Zhu, Yixuan Wei, JianFeng Wang, Zhe Gan, Zheng Zhang, Le Wang, Gang Hua, Lijuan Wang, Zicheng Liu, Han Hu

The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one.

Image Captioning Image Generation

MogaNet: Multi-order Gated Aggregation Network

6 code implementations7 Nov 2022 Siyuan Li, Zedong Wang, Zicheng Liu, Cheng Tan, Haitao Lin, Di wu, ZhiYuan Chen, Jiangbin Zheng, Stan Z. Li

Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.

3D Human Pose Estimation Image Classification +6

Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

1 code implementation17 Oct 2022 Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, Jianfeng Gao

This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years.

Few-Shot Learning Image Captioning +11

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

no code implementations5 Oct 2022 Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Zicheng Liu, Guojiang Zhao, Stan Z. Li

Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.

Classification Node Classification

OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification

1 code implementation11 Sep 2022 Siyuan Li, Zedong Wang, Zicheng Liu, Di wu, Cheng Tan, Weiyang Jin, Stan Z. Li

Data mixing, or mixup, is a data-dependent augmentation technique that has greatly enhanced the generalizability of modern deep neural networks.

Benchmarking Classification +3

Are Gradients on Graph Structure Reliable in Gray-box Attacks?

1 code implementation7 Aug 2022 Zihan Liu, Yun Luo, Lirong Wu, Siyuan Li, Zicheng Liu, Stan Z. Li

These errors arise from rough gradient usage due to the discreteness of the graph structure and from the unreliability in the meta-gradient on the graph structure.

Computational Efficiency

NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

1 code implementation20 Jul 2022 Chenfei Wu, Jian Liang, Xiaowei Hu, Zhe Gan, JianFeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan

In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos.

Image Outpainting Text-to-Image Generation +1

Should All Proposals be Treated Equally in Object Detection?

1 code implementation7 Jul 2022 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Pei Yu, Jing Yin, Lu Yuan, Zicheng Liu, Nuno Vasconcelos

We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized.

Object Object Detection

Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention

no code implementations14 Jun 2022 Quanzeng You, Jiang Wang, Peng Chu, Andre Abrantes, Zicheng Liu

We propose a consistent end-to-end video instance segmentation framework with Inter-Frame Recurrent Attention to model both the temporal instance consistency for adjacent frames and the global temporal context.

Instance Segmentation Object +3

LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

1 code implementation CVPR 2023 Linjie Li, Zhe Gan, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Ce Liu, Lijuan Wang

In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks.

Language Modelling Masked Language Modeling +6

GIT: A Generative Image-to-text Transformer for Vision and Language

2 code implementations27 May 2022 JianFeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering.

Image Captioning Image Classification +7

Cross-modal Representation Learning for Zero-shot Action Recognition

no code implementations CVPR 2022 Chung-Ching Lin, Kevin Lin, Linjie Li, Lijuan Wang, Zicheng Liu

The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent.

Action Recognition Representation Learning +1

An Intriguing Property of Geophysics Inversion

no code implementations28 Apr 2022 Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin

In particular, when dealing with the inversion from seismic data to subsurface velocity governed by a wave equation, the integral results of velocity with Gaussian kernels are linearly correlated to the integral of seismic data with sine kernels.

Geophysics

MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images

no code implementations31 Mar 2022 Xiangjun Gao, Jiaolong Yang, Jongyoo Kim, Sida Peng, Zicheng Liu, Xin Tong

For this task, we propose a simple yet effective method to train a generalizable NeRF with multiview images as conditional input.

Novel View Synthesis

Deep Frequency Filtering for Domain Generalization

no code implementations CVPR 2023 Shiqi Lin, Zhizheng Zhang, Zhipeng Huang, Yan Lu, Cuiling Lan, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Amey Parulkar, Viraj Navkal, Zhibo Chen

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.

Domain Generalization Retrieval

Harnessing Hard Mixed Samples with Decoupled Regularizer

1 code implementation NeurIPS 2023 Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

However, we found that the extra optimizing step may be redundant because label-mismatched mixed samples are informative hard mixed samples for deep models to localize discriminative features.

Data Augmentation

The Overlooked Classifier in Human-Object Interaction Recognition

no code implementations10 Mar 2022 Ying Jin, Yinpeng Chen, Lijuan Wang, JianFeng Wang, Pei Yu, Lin Liang, Jenq-Neng Hwang, Zicheng Liu

Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image.

Classification Human-Object Interaction Detection +4

SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering

no code implementations25 Jan 2022 Peixi Xiong, Quanzeng You, Pei Yu, Zicheng Liu, Ying Wu

As a multi-modality task, it is challenging since it requires not only visual and textual understanding, but also the ability to align cross-modality representations.

Question Answering Visual Question Answering

Improving Vision Transformers for Incremental Learning

no code implementations12 Dec 2021 Pei Yu, Yinpeng Chen, Ying Jin, Zicheng Liu

This paper proposes a working recipe of using Vision Transformer (ViT) in class incremental learning.

Class Incremental Learning Incremental Learning

Injecting Semantic Concepts into End-to-End Image Captioning

1 code implementation CVPR 2022 Zhiyuan Fang, JianFeng Wang, Xiaowei Hu, Lin Liang, Zhe Gan, Lijuan Wang, Yezhou Yang, Zicheng Liu

In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features.

Image Captioning

MLP Architectures for Vision-and-Language Modeling: An Empirical Study

1 code implementation8 Dec 2021 Yixin Nie, Linjie Li, Zhe Gan, Shuohang Wang, Chenguang Zhu, Michael Zeng, Zicheng Liu, Mohit Bansal, Lijuan Wang

Based on this, we ask an even bolder question: can we have an all-MLP architecture for VL modeling, where both VL fusion and the vision encoder are replaced with MLPs?

Language Modelling Visual Question Answering (VQA)

SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

1 code implementation CVPR 2022 Kevin Lin, Linjie Li, Chung-Ching Lin, Faisal Ahmed, Zhe Gan, Zicheng Liu, Yumao Lu, Lijuan Wang

Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e. g., video question answering).

Question Answering Video Captioning +2

Scaling Up Vision-Language Pre-training for Image Captioning

no code implementations CVPR 2022 Xiaowei Hu, Zhe Gan, JianFeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we present LEMON, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning.

Ranked #3 on Image Captioning on nocaps-XD entire (using extra training data)

Attribute Image Captioning

VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling

1 code implementation24 Nov 2021 Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, Zicheng Liu

Further, unlike previous studies that found pre-training tasks on video inputs (e. g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling.

Question Answering Retrieval +5

UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

1 code implementation23 Nov 2021 Zhengyuan Yang, Zhe Gan, JianFeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang

On grounded captioning, UniTAB presents a simpler solution with a single output head, and significantly outperforms state of the art in both grounding and captioning evaluations.

Image Captioning Language Modelling +5

Florence: A New Foundation Model for Computer Vision

1 code implementation22 Nov 2021 Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, JianFeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, Pengchuan Zhang

Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.

Action Classification Action Recognition In Videos +12

UFO: A UniFied TransfOrmer for Vision-Language Representation Learning

no code implementations19 Nov 2021 JianFeng Wang, Xiaowei Hu, Zhe Gan, Zhengyuan Yang, Xiyang Dai, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e. g., image or language) or multimodal inputs (e. g., the concatenation of the image and the question), for vision-language (VL) representation learning.

Image Captioning Image-text matching +9

Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering

no code implementations13 Nov 2021 Zicheng Liu, Mayank Roy, Dilip K. Prasad, Krishna Agarwal

Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost.

GenURL: A General Framework for Unsupervised Representation Learning

1 code implementation27 Oct 2021 Siyuan Li, Zicheng Liu, Zelin Zang, Di wu, ZhiYuan Chen, Stan Z. Li

Unsupervised representation learning (URL) that learns compact embeddings of high-dimensional data without supervision has achieved remarkable progress recently.

Contrastive Learning Dimensionality Reduction +4

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

no code implementations ICLR 2022 Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Xiaolei Huang, Zicheng Liu, Youzuo Lin

In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map).

Geophysics

Improving Discriminative Visual Representation Learning via Automatic Mixup

no code implementations29 Sep 2021 Siyuan Li, Zicheng Liu, Di wu, Stan Z. Li

In this paper, we decompose mixup into two sub-tasks of mixup generation and classification and formulate it for discriminative representations as class- and instance-level mixup.

Data Augmentation Representation Learning

An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

1 code implementation10 Sep 2021 Zhengyuan Yang, Zhe Gan, JianFeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, Lijuan Wang

To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA.

Ranked #19 on Visual Question Answering (VQA) on OK-VQA (using extra training data)

Image Captioning Question Answering +2

MicroNet: Improving Image Recognition with Extremely Low FLOPs

1 code implementation ICCV 2021 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, Nuno Vasconcelos

This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e. g. 5M FLOPs on ImageNet classification).

Probabilistic Model Distillation for Semantic Correspondence

1 code implementation CVPR 2021 Xin Li, Deng-Ping Fan, Fan Yang, Ao Luo, Hong Cheng, Zicheng Liu

We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.

Representation Learning Semantic correspondence

End-to-End Semi-Supervised Object Detection with Soft Teacher

8 code implementations ICCV 2021 Mengde Xu, Zheng Zhang, Han Hu, JianFeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu

This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.

Instance Segmentation object-detection +4

Compressing Visual-linguistic Model via Knowledge Distillation

no code implementations ICCV 2021 Zhiyuan Fang, JianFeng Wang, Xiaowei Hu, Lijuan Wang, Yezhou Yang, Zicheng Liu

In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model.

Image Captioning Knowledge Distillation +2

Mesh Graphormer

1 code implementation ICCV 2021 Kevin Lin, Lijuan Wang, Zicheng Liu

We present a graph-convolution-reinforced transformer, named Mesh Graphormer, for 3D human pose and mesh reconstruction from a single image.

3D Hand Pose Estimation 3D Human Pose Estimation

TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking

no code implementations1 Apr 2021 Peng Chu, Jiang Wang, Quanzeng You, Haibin Ling, Zicheng Liu

TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs.

Ranked #2 on Multi-Object Tracking on 2DMOT15 (using extra training data)

Multi-Object Tracking Multiple Object Tracking +2

Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification

no code implementations25 Mar 2021 Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Quanzeng You, Zicheng Liu, Kecheng Zheng, Zhibo Chen

Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation.

Disentanglement Domain Adaptive Person Re-Identification +2

AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

2 code implementations24 Mar 2021 Zicheng Liu, Siyuan Li, Di wu, Zihan Liu, ZhiYuan Chen, Lirong Wu, Stan Z. Li

Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i. e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework.

Classification Data Augmentation +3

Revisiting Dynamic Convolution via Matrix Decomposition

1 code implementation ICLR 2021 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Ye Yu, Lu Yuan, Zicheng Liu, Mei Chen, Nuno Vasconcelos

It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging.

Dimensionality Reduction

Stronger NAS with Weaker Predictors

1 code implementation NeurIPS 2021 Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan

We propose a paradigm shift from fitting the whole architecture space using one strong predictor, to progressively fitting a search path towards the high-performance sub-space through a set of weaker predictors.

Neural Architecture Search

Weak NAS Predictor Is All You Need

no code implementations1 Jan 2021 Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan

Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor.

Neural Architecture Search

3D Human motion anticipation and classification

no code implementations31 Dec 2020 Emad Barsoum, John Kender, Zicheng Liu

Our model learns to predict multiple future sequences of human poses from the same input sequence.

Action Recognition Classification +6

MiniVLM: A Smaller and Faster Vision-Language Model

no code implementations13 Dec 2020 JianFeng Wang, Xiaowei Hu, Pengchuan Zhang, Xiujun Li, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu

We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\%$, compared to a baseline model.

Language Modelling

MicroNet: Towards Image Recognition with Extremely Low FLOPs

no code implementations24 Nov 2020 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, Nuno Vasconcelos

In this paper, we present MicroNet, which is an efficient convolutional neural network using extremely low computational cost (e. g. 6 MFLOPs on ImageNet classification).

Semantic Change Detection with Asymmetric Siamese Networks

1 code implementation12 Oct 2020 Kunping Yang, Gui-Song Xia, Zicheng Liu, Bo Du, Wen Yang, Marcello Pelillo, Liangpei Zhang

Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.

Change Detection Management

VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning

no code implementations28 Sep 2020 Xiaowei Hu, Xi Yin, Kevin Lin, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu

It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps).

Image Captioning Object +1

Deep Clustering and Representation Learning that Preserves Geometric Structures

no code implementations28 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

To overcome the problem that clusteringoriented losses may deteriorate the geometric structure of embeddings in the latent space, an isometric loss is proposed for preserving intra-manifold structure locally and a ranking loss for inter-manifold structure globally.

Clustering Deep Clustering +1

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation

1 code implementation21 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss may corrupt the local and global structure of the latent space.

Clustering Deep Clustering +1

Dynamic ReLU

2 code implementations ECCV 2020 Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dong-Dong Chen, Lu Yuan, Zicheng Liu

Rectified linear units (ReLU) are commonly used in deep neural networks.

Learning Nonparametric Human Mesh Reconstruction from a Single Image without Ground Truth Meshes

no code implementations28 Feb 2020 Kevin Lin, Lijuan Wang, Ying Jin, Zicheng Liu, Ming-Ting Sun

Experimental results on multiple public datasets show that without using 3D ground truth meshes, the proposed approach outperforms the previous state-of-the-art approaches that require ground truth meshes for training.

Segmentation

Dynamic Convolution: Attention over Convolution Kernels

5 code implementations CVPR 2020 Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dong-Dong Chen, Lu Yuan, Zicheng Liu

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability.

Image Classification Keypoint Detection

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

3 code implementations11 Jul 2019 Kevin Lin, Lijuan Wang, Kun Luo, Yinpeng Chen, Zicheng Liu, Ming-Ting Sun

On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.

 Ranked #1 on Human Part Segmentation on PASCAL-Part (using extra training data)

Domain Adaptation Human Part Segmentation +3

Large Scale Incremental Learning

4 code implementations CVPR 2019 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Yun Fu

We believe this is because of the combination of two factors: (a) the data imbalance between the old and new classes, and (b) the increasing number of visually similar classes.

Class Incremental Learning Incremental Learning

Rethinking Classification and Localization for Object Detection

2 code implementations CVPR 2020 Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu

Two head structures (i. e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks.

Classification General Classification +3

Incremental Classifier Learning with Generative Adversarial Networks

no code implementations2 Feb 2018 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Zhengyou Zhang, Yun Fu

To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation.

General Classification

HP-GAN: Probabilistic 3D human motion prediction via GAN

3 code implementations27 Nov 2017 Emad Barsoum, John Kender, Zicheng Liu

Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.

Autonomous Vehicles Human motion prediction +6

A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories

no code implementations10 Sep 2016 Weiyao Lin, Yang Zhou, Hongteng Xu, Junchi Yan, Mingliang Xu, Jianxin Wu, Zicheng Liu

Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.

3D Action Recognition Anomaly Detection +2

Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation

no code implementations CVPR 2013 Luming Zhang, Mingli Song, Zicheng Liu, Xiao Liu, Jiajun Bu, Chun Chen

Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.

Image Segmentation Segmentation +2

Tensor-Based Human Body Modeling

no code implementations CVPR 2013 Yinpeng Chen, Zicheng Liu, Zhengyou Zhang

In this paper, we present a novel approach to model 3D human body with variations on both human shape and pose, by exploring a tensor decomposition technique.

3D Reconstruction Tensor Decomposition

HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences

no code implementations CVPR 2013 Omar Oreifej, Zicheng Liu

In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates.

Activity Recognition

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