Search Results for author: Binbin Lin

Found 27 papers, 10 papers with code

NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection

1 code implementation22 Feb 2024 Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, Deng Cai, Wanli Ouyang

We project the freely available 3D segmentation annotations onto the 2D plane and leverage the corresponding 2D semantic maps as the supervision signal, significantly enhancing the semantic awareness of multi-view detectors.

Depth Estimation Depth Prediction +1

Model Compression and Efficient Inference for Large Language Models: A Survey

no code implementations15 Feb 2024 Wenxiao Wang, Wei Chen, Yicong Luo, Yongliu Long, Zhengkai Lin, Liye Zhang, Binbin Lin, Deng Cai, Xiaofei He

However, Large language models have two prominent characteristics compared to smaller models: (1) Most of compression algorithms require finetuning or even retraining the model after compression.

Knowledge Distillation Model Compression +1

Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

no code implementations21 Sep 2023 Ping Li, Yu Zhang, Li Yuan, Huaxin Xiao, Binbin Lin, Xianghua Xu

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge.

Semantic Segmentation Unsupervised Video Object Segmentation +1

NormKD: Normalized Logits for Knowledge Distillation

1 code implementation1 Aug 2023 Zhihao Chi, Tu Zheng, Hengjia Li, Zheng Yang, Boxi Wu, Binbin Lin, Deng Cai

In this paper, we restudy the hyper-parameter temperature and figure out its incapability to distill the knowledge from each sample sufficiently when it is a single value.

Image Classification Knowledge Distillation

PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer

1 code implementation CVPR 2023 Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang

The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries.

Autonomous Driving Quantization

Neural Collapse Inspired Federated Learning with Non-iid Data

no code implementations27 Mar 2023 Chenxi Huang, Liang Xie, Yibo Yang, Wenxiao Wang, Binbin Lin, Deng Cai

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server.

Federated Learning

CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention

1 code implementation13 Mar 2023 Wenxiao Wang, Wei Chen, Qibo Qiu, Long Chen, Boxi Wu, Binbin Lin, Xiaofei He, Wei Liu

On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features.

Image Classification Instance Segmentation +3

OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection

1 code implementation20 Dec 2022 Chenxi Huang, Tong He, Haidong Ren, Wenxiao Wang, Binbin Lin, Deng Cai

Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training.

Monocular 3D Object Detection object-detection

GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds

1 code implementation CVPR 2023 Honghui Yang, Tong He, Jiaheng Liu, Hua Chen, Boxi Wu, Binbin Lin, Xiaofei He, Wanli Ouyang

In contrast to previous 3D MAE frameworks, which either design a complex decoder to infer masked information from maintained regions or adopt sophisticated masking strategies, we instead propose a much simpler paradigm.

Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

no code implementations14 Nov 2022 Xiaopei Wu, Yang Zhao, Liang Peng, Hua Chen, Xiaoshui Huang, Binbin Lin, Haifeng Liu, Deng Cai, Wanli Ouyang

When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.

3D Object Detection Data Augmentation +2

SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks

no code implementations22 Dec 2021 Weigang Lu, Yibing Zhan, Binbin Lin, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao

In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.

Link Prediction Node Classification

CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention

3 code implementations ICLR 2022 Wenxiao Wang, Lu Yao, Long Chen, Binbin Lin, Deng Cai, Xiaofei He, Wei Liu

On the one hand, CEL blends each embedding with multiple patches of different scales, providing the self-attention module itself with cross-scale features.

Image Classification Instance Segmentation +4

Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation

no code implementations30 Jul 2014 Binbin Lin, Qingyang Li, Qian Sun, Ming-Jun Lai, Ian Davidson, Wei Fan, Jieping Ye

The effectiveness of gene expression pattern annotation relies on the quality of feature representation.

Geodesic Distance Function Learning via Heat Flow on Vector Fields

no code implementations1 May 2014 Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself.

Multi-task Vector Field Learning

no code implementations NeurIPS 2012 Binbin Lin, Sen yang, Chiyuan Zhang, Jieping Ye, Xiaofei He

MTVFL has the following key properties: (1) the vector fields we learned are close to the gradient fields of the prediction functions; (2) within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace; (3) the vector fields from all tasks share a low dimensional subspace.

Multi-Task Learning

Semi-supervised Regression via Parallel Field Regularization

no code implementations NeurIPS 2011 Binbin Lin, Chiyuan Zhang, Xiaofei He

To achieve this goal, we show that the second order smoothness measures the linearity of the function, and the gradient field of a linear function has to be a parallel vector field.


Cannot find the paper you are looking for? You can Submit a new open access paper.