Search Results for author: Dequan Wang

Found 39 papers, 24 papers with code

Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models

1 code implementation7 Oct 2024 Zhenyu Wang, Yi Xu, Dequan Wang, Lingfeng Zhou, Yiqi Zhou

The recent wave of artificial intelligence, epitomized by large language models (LLMs), has presented opportunities and challenges for methodological innovation in political science, sparking discussions on a potential paradigm shift in the social sciences.

Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models

1 code implementation1 Aug 2024 Juntu Zhao, Junyu Deng, Yixin Ye, Chongxuan Li, Zhijie Deng, Dequan Wang

The root of such misalignment is attributed to the confusion in the latent semantic space of text-to-image diffusion models, and hence we refer to the "a tea cup of iced coke" phenomenon as Latent Concept Misalignment (LC-Mis).

Direct Preference Knowledge Distillation for Large Language Models

no code implementations28 Jun 2024 Yixing Li, Yuxian Gu, Li Dong, Dequan Wang, Yu Cheng, Furu Wei

Meanwhile, we prove the value and effectiveness of the introduced implicit reward and output preference in KD through experiments and theoretical analysis.

Knowledge Distillation

OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine

no code implementations28 Feb 2024 Xiaosong Wang, Xiaofan Zhang, Guotai Wang, Junjun He, Zhongyu Li, Wentao Zhu, Yi Guo, Qi Dou, Xiaoxiao Li, Dequan Wang, Liang Hong, Qicheng Lao, Tong Ruan, Yukun Zhou, Yixue Li, Jie Zhao, Kang Li, Xin Sun, Lifeng Zhu, Shaoting Zhang

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.

Transfer Learning

An Extensible Framework for Open Heterogeneous Collaborative Perception

1 code implementation25 Jan 2024 Yifan Lu, Yue Hu, Yiqi Zhong, Dequan Wang, Yanfeng Wang, Siheng Chen

In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost?

Data-Centric Foundation Models in Computational Healthcare: A Survey

1 code implementation4 Jan 2024 Yunkun Zhang, Jin Gao, Zheling Tan, Lingfeng Zhou, Kexin Ding, Mu Zhou, Shaoting Zhang, Dequan Wang

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare.

Ethics Survey

BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference

1 code implementation17 Oct 2023 Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng

In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference.

Bayesian Inference Image Generation

Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

no code implementations12 Mar 2023 Huahui Yi, Ziyuan Qin, Qicheng Lao, Wei Xu, Zekun Jiang, Dequan Wang, Shaoting Zhang, Kang Li

Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.

Continual Learning

Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption

no code implementations CVPR 2023 Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang

We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model.

Test-time Adaptation

Back to the Source: Diffusion-Driven Test-Time Adaptation

1 code implementation7 Jul 2022 Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang

We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model.

Test-time Adaptation

GACT: Activation Compressed Training for Generic Network Architectures

1 code implementation22 Jun 2022 Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael Mahoney, Alvin Cheung

Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.

Contrastive Test-Time Adaptation

1 code implementation CVPR 2022 Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi

We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels.

Contrastive Learning Test-time Adaptation +1

On-target Adaptation

1 code implementation2 Sep 2021 Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, Trevor Darrell

Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain.

Domain Adaptation

BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud

no code implementations19 Jun 2020 Mong H. Ng, Kaahan Radia, Jianfei Chen, Dequan Wang, Ionel Gog, Joseph E. Gonzalez

Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in the scene.

Bird's-Eye View Semantic Segmentation Transfer Learning

Algorithm-hardware Co-design for Deformable Convolution

2 code implementations19 Feb 2020 Qijing Huang, Dequan Wang, Yizhao Gao, Yaohui Cai, Zhen Dong, Bichen Wu, Kurt Keutzer, John Wawrzynek

In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.

Image Classification Instance Segmentation +4

Blurring Structure and Learning to Optimize and Adapt Receptive Fields

no code implementations25 Sep 2019 Evan Shelhamer, Dequan Wang, Trevor Darrell

Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.

Semantic Segmentation

Dynamic Scale Inference by Entropy Minimization

no code implementations8 Aug 2019 Dequan Wang, Evan Shelhamer, Bruno Olshausen, Trevor Darrell

Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field.

Diversity Semantic Segmentation

Monocular Plan View Networks for Autonomous Driving

no code implementations16 May 2019 Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.

3D Object Detection Autonomous Driving +1

Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields

no code implementations25 Apr 2019 Evan Shelhamer, Dequan Wang, Trevor Darrell

Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.

Semantic Segmentation

Joint Monocular 3D Vehicle Detection and Tracking

1 code implementation ICCV 2019 Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, Fisher Yu

The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.

3D Object Detection 3D Pose Estimation +4

Deep Object-Centric Policies for Autonomous Driving

no code implementations13 Nov 2018 Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways.

Autonomous Driving Object

VisDA: The Visual Domain Adaptation Challenge

2 code implementations18 Oct 2017 Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, Kate Saenko

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.

General Classification Image Classification +3

Deep Layer Aggregation

6 code implementations CVPR 2018 Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell

We augment standard architectures with deeper aggregation to better fuse information across layers.

Image Classification

Iterative Object and Part Transfer for Fine-Grained Recognition

no code implementations29 Mar 2017 Zhiqiang Shen, Yu-Gang Jiang, Dequan Wang, xiangyang xue

On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.

Object

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

3 code implementations8 Dec 2016 Judy Hoffman, Dequan Wang, Fisher Yu, Trevor Darrell

In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.

Semantic Segmentation Synthetic-to-Real Translation

Learning to Point and Count

no code implementations8 Dec 2015 Jie Shao, Dequan Wang, xiangyang xue, Zheng Zhang

This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.

General Classification

Multiple Granularity Descriptors for Fine-Grained Categorization

no code implementations ICCV 2015 Dequan Wang, Zhiqiang Shen, Jie Shao, Wei zhang, xiangyang xue, Zheng Zhang

Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.

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