Search Results for author: Haojun Jiang

Found 9 papers, 7 papers with code

LocalGCL: Local-aware Contrastive Learning for Graphs

no code implementations27 Feb 2024 Haojun Jiang, Jiawei Sun, Jie Li, Chentao Wu

Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings.

Contrastive Learning Graph Representation Learning +1

Joint Representation Learning for Text and 3D Point Cloud

no code implementations18 Jan 2023 Rui Huang, Xuran Pan, Henry Zheng, Haojun Jiang, Zhifeng Xie, Shiji Song, Gao Huang

During the pre-training stage, we establish the correspondence of images and point clouds based on the readily available RGB-D data and use contrastive learning to align the image and point cloud representations.

Contrastive Learning Instance Segmentation +4

Deep Incubation: Training Large Models by Divide-and-Conquering

3 code implementations ICCV 2023 Zanlin Ni, Yulin Wang, Jiangwei Yu, Haojun Jiang, Yue Cao, Gao Huang

In this paper, we present Deep Incubation, a novel approach that enables the efficient and effective training of large models by dividing them into smaller sub-modules that can be trained separately and assembled seamlessly.

Image Segmentation object-detection +2

Cross-Modal Adapter for Text-Video Retrieval

1 code implementation17 Nov 2022 Haojun Jiang, Jianke Zhang, Rui Huang, Chunjiang Ge, Zanlin Ni, Jiwen Lu, Jie zhou, Shiji Song, Gao Huang

However, as pre-trained models are scaling up, fully fine-tuning them on text-video retrieval datasets has a high risk of overfitting.

Retrieval Video Retrieval

Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding

1 code implementation CVPR 2022 Haojun Jiang, Yuanze Lin, Dongchen Han, Shiji Song, Gao Huang

Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module.

Language Modelling Natural Language Queries +1

Glance and Focus Networks for Dynamic Visual Recognition

1 code implementation9 Jan 2022 Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, Shiji Song

Spatial redundancy widely exists in visual recognition tasks, i. e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand.

Image Classification Video Recognition

Adaptive Focus for Efficient Video Recognition

1 code implementation ICCV 2021 Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang

In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency.

Computational Efficiency Video Recognition

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