1 code implementation • 7 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.
1 code implementation • 2 Aug 2024 • Jin Gao, Lei Gan, Yuankai Li, Yixin Ye, Dequan Wang
Large multimodal models (LMMs) excel in adhering to human instructions.
1 code implementation • 1 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).
no code implementations • 28 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.
no code implementations • 28 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.
1 code implementation • 25 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?
1 code implementation • 4 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.
1 code implementation • 17 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.
2 code implementations • 27 Jul 2023 • Yunkun Zhang, Jin Gao, Mu Zhou, Xiaosong Wang, Yu Qiao, Shaoting Zhang, Dequan Wang
In this paper, we propose to Connect Image and Text Embeddings (CITE) to enhance pathological image classification.
1 code implementation • 16 Jun 2023 • Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, Shaoting Zhang
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
no code implementations • 12 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.
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.
1 code implementation • 19 Sep 2022 • Fangyu Wu, Dequan Wang, Minjune Hwang, Chenhui Hao, Jiawei Lu, Jiamu Zhang, Christopher Chou, Trevor Darrell, Alexandre Bayen
Decentralized multiagent planning has been an important field of research in robotics.
1 code implementation • 7 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.
1 code implementation • 22 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.
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.
1 code implementation • 2 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.
2 code implementations • NeurIPS 2021 • Dequan Wang, An Ju, Evan Shelhamer, David Wagner, Trevor Darrell
Adversarial attacks optimize against models to defeat defenses.
4 code implementations • 29 Apr 2021 • Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph E. Gonzalez
On all these tasks, ActNN compresses the activation to 2 bits on average, with negligible accuracy loss.
no code implementations • 19 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.
2 code implementations • ICLR 2021 • Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell
A model must adapt itself to generalize to new and different data during testing.
3 code implementations • 12 Jun 2020 • Zhen Dong, Dequan Wang, Qijing Huang, Yizhao Gao, Yaohui Cai, Tian Li, Bichen Wu, Kurt Keutzer, John Wawrzynek
Deploying deep learning models on embedded systems has been challenging due to limited computing resources.
2 code implementations • 19 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.
no code implementations • 25 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.
no code implementations • 8 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.
no code implementations • 16 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.
no code implementations • 25 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.
76 code implementations • 16 Apr 2019 • Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
We model an object as a single point --- the center point of its bounding box.
no code implementations • ICLR Workshop LLD 2019 • Evan Shelhamer, Dequan Wang, Trevor Darrell
The visual world is vast and varied, but its variations divide into structured and unstructured factors.
2 code implementations • ICLR 2019 • Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Nießner
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
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.
Ranked #17 on Multiple Object Tracking on KITTI Test (Online Methods) (MOTA metric)
no code implementations • 13 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.
2 code implementations • 18 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.
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.
no code implementations • 29 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.
3 code implementations • 8 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.
Ranked #2 on Image-to-Image Translation on SYNTHIA Fall-to-Winter
no code implementations • 8 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.
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.
no code implementations • CVPR 2015 • Wei Zhang, Sheng Zeng, Dequan Wang, xiangyang xue
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.