Search Results for author: Haofei Zhang

Found 17 papers, 12 papers with code

Enhancing Thermal Infrared Tracking with Natural Language Modeling and Coordinate Sequence Generation

1 code implementation11 Jul 2024 Miao Yan, Ping Zhang, Haofei Zhang, Ruqian Hao, Juanxiu Liu, Xiaoyang Wang, Lin Liu

In this paper, to address these issues, we apply natural language modeling to TIR tracking and propose a novel model called NLMTrack, which enhances the utilization of coordinate and temporal information.

Language Modelling

On the Concept Trustworthiness in Concept Bottleneck Models

1 code implementation21 Mar 2024 Qihan Huang, Jie Song, Jingwen Hu, Haofei Zhang, Yong Wang, Mingli Song

Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck.

Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios

no code implementations4 Feb 2024 Yuxin Wang, Zunlei Feng, Haofei Zhang, Yang Gao, Jie Lei, Li Sun, Mingli Song

Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.

Message-passing selection: Towards interpretable GNNs for graph classification

no code implementations3 Jun 2023 Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song

In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.

Graph Classification

Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization

1 code implementation31 May 2023 KaiXuan Chen, Shunyu Liu, Tongtian Zhu, Tongya Zheng, Haofei Zhang, Zunlei Feng, Jingwen Ye, Mingli Song

Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.

Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation

1 code implementation CVPR 2023 Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song

Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.

Knowledge Distillation

Schema Inference for Interpretable Image Classification

1 code implementation12 Mar 2023 Haofei Zhang, Mengqi Xue, Xiaokang Liu, KaiXuan Chen, Jie Song, Mingli Song

In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema.

Classification Graph Matching +1

Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks

1 code implementation ICCV 2023 Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, Mingli Song

Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.

A Survey of Neural Trees

1 code implementation7 Sep 2022 Haoling Li, Jie Song, Mengqi Xue, Haofei Zhang, Jingwen Ye, Lechao Cheng, Mingli Song

This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability.

ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition

1 code implementation22 Aug 2022 Mengqi Xue, Qihan Huang, Haofei Zhang, Lechao Cheng, Jie Song, Minghui Wu, Mingli Song

The global prototypes are adopted to provide the global view of objects to guide local prototypes to concentrate on the foreground while eliminating the influence of the background.

Decision Making Explainable artificial intelligence +1

Meta-attention for ViT-backed Continual Learning

1 code implementation CVPR 2022 Mengqi Xue, Haofei Zhang, Jie Song, Mingli Song

Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks.

Continual Learning

Knowledge Amalgamation for Object Detection with Transformers

1 code implementation7 Mar 2022 Haofei Zhang, Feng Mao, Mengqi Xue, Gongfan Fang, Zunlei Feng, Jie Song, Mingli Song

Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.

Object object-detection +1

Up to 100$\times$ Faster Data-free Knowledge Distillation

2 code implementations12 Dec 2021 Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, Mingli Song

At the heart of our approach is a novel strategy to reuse the shared common features in training data so as to synthesize different data instances.

Data-free Knowledge Distillation

Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training

1 code implementation CVPR 2022 Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli Song

Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV).

A Survey of Deep Learning for Low-Shot Object Detection

no code implementations6 Dec 2021 Qihan Huang, Haofei Zhang, Mengqi Xue, Jie Song, Mingli Song

Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task.

Few-Shot Learning Few-Shot Object Detection +6

Tree-Like Decision Distillation

no code implementations CVPR 2021 Jie Song, Haofei Zhang, Xinchao Wang, Mengqi Xue, Ying Chen, Li Sun, DaCheng Tao, Mingli Song

Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.

Decision Making Knowledge Distillation

Impression Space from Deep Template Network

no code implementations10 Jul 2020 Gongfan Fang, Xinchao Wang, Haofei Zhang, Jie Song, Mingli Song

This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression.

Image Generation Translation

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