Search Results for author: Jiawei Ma

Found 17 papers, 11 papers with code

How to Use Diffusion Priors under Sparse Views?

1 code implementation3 Dec 2024 Qisen Wang, Yifan Zhao, Jiawei Ma, Jia Li

However, the diffusion model, as an external prior that can directly provide visual supervision, has always underperformed in sparse-view 3D reconstruction using Score Distillation Sampling (SDS) due to the low information entropy of sparse views compared to text, leading to optimization challenges caused by mode deviation.

3D Reconstruction Novel View Synthesis

WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization

no code implementations28 May 2024 Jiawei Ma, Yulei Niu, Shiyuan Huang, Guangxing Han, Shih-Fu Chang

Language has been useful in extending the vision encoder to data from diverse distributions without empirical discovery in training domains.

Single-Source Domain Generalization

MoDE: CLIP Data Experts via Clustering

1 code implementation CVPR 2024 Jiawei Ma, Po-Yao Huang, Saining Xie, Shang-Wen Li, Luke Zettlemoyer, Shih-Fu Chang, Wen-tau Yih, Hu Xu

The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data.

Clustering Image Classification +1

Supervised Masked Knowledge Distillation for Few-Shot Transformers

1 code implementation CVPR 2023 Han Lin, Guangxing Han, Jiawei Ma, Shiyuan Huang, Xudong Lin, Shih-Fu Chang

Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features.

Few-Shot Learning Inductive Bias +1

DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection

1 code implementation CVPR 2023 Jiawei Ma, Yulei Niu, Jincheng Xu, Shiyuan Huang, Guangxing Han, Shih-Fu Chang

Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data.

Few-Shot Object Detection object-detection

TempCLR: Temporal Alignment Representation with Contrastive Learning

1 code implementation28 Dec 2022 Yuncong Yang, Jiawei Ma, Shiyuan Huang, Long Chen, Xudong Lin, Guangxing Han, Shih-Fu Chang

For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly.

Contrastive Learning Dynamic Time Warping +7

Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting

no code implementations16 Apr 2022 Guangxing Han, Long Chen, Jiawei Ma, Shiyuan Huang, Rama Chellappa, Shih-Fu Chang

Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning.

Few-Shot Learning Few-Shot Object Detection +3

Few-Shot Object Detection with Fully Cross-Transformer

1 code implementation CVPR 2022 Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, Shih-Fu Chang

Inspired by the recent work on vision transformers and vision-language transformers, we propose a novel Fully Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer into both the feature backbone and detection head.

Few-Shot Object Detection Metric Learning +2

Partner-Assisted Learning for Few-Shot Image Classification

no code implementations ICCV 2021 Jiawei Ma, Hanchen Xie, Guangxing Han, Shih-Fu Chang, Aram Galstyan, Wael Abd-Almageed

In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.

Classification Few-Shot Image Classification +1

Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment

2 code implementations15 Apr 2021 Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, Shih-Fu Chang

To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection.

Few-Shot Learning Few-Shot Object Detection +3

Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition

1 code implementation CVPR 2022 Shiyuan Huang, Jiawei Ma, Guangxing Han, Shih-Fu Chang

In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process.

Few-Shot Learning Open Set Learning

Deep Tensor ADMM-Net for Snapshot Compressive Imaging

no code implementations ICCV 2019 Jiawei Ma, Xiao-Yang Liu, Zheng Shou, Xin Yuan

In this paper, we propose a deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds.

Decoder SSIM

CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation

2 code implementations23 May 2019 Jiawei Ma, Zheng Shou, Alireza Zareian, Hassan Mansour, Anthony Vetro, Shih-Fu Chang

In order to jointly capture the self-attention across multiple dimensions, including time, location and the sensor measurements, while maintain low computational complexity, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.

Imputation Machine Translation +3

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