Search Results for author: Pengfei Fang

Found 18 papers, 4 papers with code

Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

no code implementations14 Jun 2022 Yuan Feng, Yaojun Hu, Pengfei Fang, Yanhong Yang, Sheng Liu, ShengYong Chen

However, jointly removing the rain and haze in scene images is ill-posed and challenging, where the existence of haze and rain and the change of atmosphere light, can both degrade the scene information.

Autonomous Driving

GOSS: Towards Generalized Open-set Semantic Segmentation

no code implementations23 Mar 2022 Jie Hong, Weihao Li, Junlin Han, Jiyang Zheng, Pengfei Fang, Mehrtash Harandi, Lars Petersson

In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS).

Semantic Segmentation

Towards Automated Real-time Evaluation in Text-based Counseling

no code implementations7 Mar 2022 Anqi Li, Jingsong Ma, Lizhi Ma, Pengfei Fang, Hongliang He, Zhenzhong Lan

However, these methods often demand large scale and high quality counseling data, which are difficult to collect.

Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning

no code implementations7 Dec 2021 Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tom Drummond, Mehrtash Harandi

A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.

Few-Shot Learning

Adaptive Poincaré Point to Set Distance for Few-Shot Classification

no code implementations3 Dec 2021 Rongkai Ma, Pengfei Fang, Tom Drummond, Mehrtash Harandi

To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively and based on the constellation of the points.

Few-Shot Learning

TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency

1 code implementation11 Oct 2021 Lin Cheng, Pengfei Fang, Yanjie Liang, Liao Zhang, Chunhua Shen, Hanzi Wang

Inspired by those observations, we propose a novel visual saliency method, termed Target-Selective Gradient Backprop (TSGB), which leverages rectification operations to effectively emphasize target classes and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps.

Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

no code implementations20 Sep 2021 Jieming Zhou, Tong Zhang, Pengfei Fang, Lars Petersson, Mehrtash Harandi

The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbors.

Blind Image Decomposition

1 code implementation25 Aug 2021 Junlin Han, Weihao Li, Pengfei Fang, Chunyi Sun, Jie Hong, Mohammad Ali Armin, Lars Petersson, Hongdong Li

We present and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown.

Rain Removal

Panoramic-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems

1 code implementation2 Jun 2021 Chiyu Song, Hongliang He, Haofei Yu, Huachuan Qiu, Pengfei Fang, Zhenzhong Lan

The current state-of-the-art methods are mainly based on the encoding paradigm called Cross-Encoder, which separately encodes each context-response pair and ranks the responses according to their fitness scores.

Reinforced Attention for Few-Shot Learning and Beyond

no code implementations CVPR 2021 Jie Hong, Pengfei Fang, Weihao Li, Tong Zhang, Christian Simon, Mehrtash Harandi, Lars Petersson

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images.

Few-Shot Learning Image Classification +1

Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

no code implementations CVPR 2021 Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, Mehrtash Harandi

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner.

class-incremental learning Incremental Learning +2

Kernel Methods in Hyperbolic Spaces

no code implementations ICCV 2021 Pengfei Fang, Mehrtash Harandi, Lars Petersson

However, working in hyperbolic spaces is not without difficulties as a result of its curved geometry (e. g., computing the Frechet mean of a set of points requires an iterative algorithm).

Few-Shot Learning Knowledge Distillation +3

Set Augmented Triplet Loss for Video Person Re-Identification

no code implementations2 Nov 2020 Pengfei Fang, Pan Ji, Lars Petersson, Mehrtash Harandi

Modern video person re-identification (re-ID) machines are often trained using a metric learning approach, supervised by a triplet loss.

Metric Learning Video-Based Person Re-Identification

Channel Recurrent Attention Networks for Video Pedestrian Retrieval

no code implementations7 Oct 2020 Pengfei Fang, Pan Ji, Jieming Zhou, Lars Petersson, Mehrtash Harandi

Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks.

Person Retrieval

Cross-Correlated Attention Networks for Person Re-Identification

no code implementations17 Jun 2020 Jieming Zhou, Soumava Kumar Roy, Pengfei Fang, Mehrtash Harandi, Lars Petersson

Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -- to name a few -- when the task of person re-identification is considered.

Person Re-Identification

Bilinear Attention Networks for Person Retrieval

no code implementations ICCV 2019 Pengfei Fang, Jieming Zhou, Soumava Kumar Roy, Lars Petersson, Mehrtash Harandi

This paper investigates a novel Bilinear attention (Bi-attention) block, which discovers and uses second order statistical information in an input feature map, for the purpose of person retrieval.

Benchmark Person Retrieval

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