1 code implementation • 24 May 2024 • Zicheng Wang, Zhenghao Chen, Yiming Wu, Zhen Zhao, Luping Zhou, Dong Xu
In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational efficiency of Mamba for enhanced point cloud analysis.
no code implementations • 24 May 2024 • Yiming Wu, Hangfei Li, Fangfang Wang, Yilong Zhang, Ronghua Liang
In response, we propose a Self-distilled Dynamic Fusion Network to compose the multi-granularity features dynamically by considering the consistency of routing path and modality-specific information simultaneously.
no code implementations • 15 May 2024 • Qihe Pan, Zicheng Wang, Zhen Zhao, Yiming Wu, Sifan Long, Haoran Liang, Ronghua Liang
In this paper, we delve into a new task known as small object editing (SOE), which focuses on text-based image inpainting within a constrained, small-sized area.
1 code implementation • 25 Apr 2024 • Sifan Long, Linbin Wang, Zhen Zhao, Zichang Tan, Yiming Wu, Shengsheng Wang, Jingdong Wang
In light of this, we propose Training-Free Unsupervised Prompts (TFUP), which maximally preserves the inherent representation capabilities and enhances them with a residual connection to similarity-based prediction probabilities in a training-free and labeling-free manner.
1 code implementation • 27 Nov 2023 • Zicheng Wang, Zhen Zhao, Yiming Wu, Luping Zhou, Dong Xu
Unlike previous works that focus on feature extractor adaptation, our PTSFA approach focuses on classifier adaptation.
1 code implementation • 28 Jul 2023 • Li Li, Wei Ji, Yiming Wu, Mengze Li, You Qin, Lina Wei, Roger Zimmermann
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on Panoptic Scene Graph Generation on PSG Dataset
no code implementations • 25 Jul 2023 • Yiming Wu, Ruixiang Li, Zequn Qin, Xinhai Zhao, Xi Li
In this work, we propose to explicitly model heights in the BEV space, which needs no extra data like LiDAR and can fit arbitrary camera rigs and types compared to modeling depths.
no code implementations • 26 Dec 2022 • Wei Ji, Long Chen, Yinwei Wei, Yiming Wu, Tat-Seng Chua
In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module.
no code implementations • 21 May 2022 • Yangkai Du, Tengfei Ma, Lingfei Wu, Yiming Wu, Xuhong Zhang, Bo Long, Shouling Ji
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #25 on Relation Extraction on DocRED
no code implementations • 12 May 2022 • Xintian Wu, Huanyu Wang, Yiming Wu, Xi Li
To transfer knowledge between discriminators, we design a multi-level discriminant knowledge distillation from the source discriminator to the target discriminator on both the real and fake samples.
no code implementations • 12 May 2022 • Xintian Wu, Qihang Zhang, Yiming Wu, Huanyu Wang, Songyuan Li, Lingyun Sun, Xi Li
Formulated as a conditional generation problem, face animation aims at synthesizing continuous face images from a single source image driven by a set of conditional face motion.
1 code implementation • 12 Oct 2021 • Yiming Wu, Xintian Wu, Xi Li, Jian Tian
As a challenging task, unsupervised person ReID aims to match the same identity with query images which does not require any labeled information.
1 code implementation • 14 May 2020 • Yiming Wu, Tristan Carsault, Eita Nakamura, Kazuyoshi Yoshii
In contrast, we propose a unified generative and discriminative approach in the framework of amortized variational inference.
no code implementations • CVPR 2020 • Yifeng Chen, Guangchen Lin, Songyuan Li, Bourahla Omar, Yiming Wu, Fangfang Wang, Junyi Feng, Mingliang Xu, Xi Li
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
1 code implementation • 5 Sep 2019 • Yiming Wu, Omar El Farouk Bourahla, Xi Li, Fei Wu, Qi Tian, Xue Zhou
While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features.
Ranked #3 on Person Re-Identification on PRID2011
Graph Representation Learning Video-Based Person Re-Identification
no code implementations • 3 Jul 2019 • Saeid Soheily Khah, Yiming Wu
In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance.
1 code implementation • CVPR 2019 • Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha
We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.
5 code implementations • CVPR 2019 • Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer
Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Ranked #894 on Image Classification on <h2>oi</h2>
no code implementations • 24 Nov 2018 • Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
no code implementations • 6 Oct 2018 • Yiming Wu, Wei Ji, Xi Li, Gang Wang, Jianwei Yin, Fei Wu
As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images.