Feature Completion for Occluded Person Re-Identification
- URL: http://arxiv.org/abs/2106.12733v1
- Date: Thu, 24 Jun 2021 02:40:40 GMT
- Title: Feature Completion for Occluded Person Re-Identification
- Authors: Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan and
Xilin Chen
- Abstract summary: RFC block can recover semantics of occluded regions in feature space.
SRFC exploits the long-range spatial contexts from non-occluded regions to predict the features of occluded regions.
TRFC module captures the long-term temporal contexts to refine the prediction of SRFC.
- Score: 138.5671859358049
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Person re-identification (reID) plays an important role in computer vision.
However, existing methods suffer from performance degradation in occluded
scenes. In this work, we propose an occlusion-robust block, Region Feature
Completion (RFC), for occluded reID. Different from most previous works that
discard the occluded regions, RFC block can recover the semantics of occluded
regions in feature space. Firstly, a Spatial RFC (SRFC) module is developed.
SRFC exploits the long-range spatial contexts from non-occluded regions to
predict the features of occluded regions. The unit-wise prediction task leads
to an encoder/decoder architecture, where the region-encoder models the
correlation between non-occluded and occluded region, and the region-decoder
utilizes the spatial correlation to recover occluded region features. Secondly,
we introduce Temporal RFC (TRFC) module which captures the long-term temporal
contexts to refine the prediction of SRFC. RFC block is lightweight, end-to-end
trainable and can be easily plugged into existing CNNs to form RFCnet.
Extensive experiments are conducted on occluded and commonly holistic reID
benchmarks. Our method significantly outperforms existing methods on the
occlusion datasets, while remains top even superior performance on holistic
datasets. The source code is available at
https://github.com/blue-blue272/OccludedReID-RFCnet.
Related papers
- LCP-Fusion: A Neural Implicit SLAM with Enhanced Local Constraints and Computable Prior [22.948360879064758]
LCP-Fusion is a neural implicit SLAM system with enhanced local constraints and computable prior.
We show that our method achieve better localization accuracy and reconstruction consistency than existing RGB-D implicit SLAM.
arXiv Detail & Related papers (2024-11-06T02:05:44Z) - Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization [60.899082019130766]
We introduce a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization.
FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions.
PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN.
arXiv Detail & Related papers (2024-07-23T15:07:52Z) - Decoupled Local Aggregation for Point Cloud Learning [12.810517967372043]
We propose to decouple the explicit modelling of spatial relations from local aggregation.
We present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed.
DeLA achieves over 90% overall accuracy on ScanObjectNN and 74% mIoU on S3DIS Area 5.
arXiv Detail & Related papers (2023-08-31T08:21:29Z) - Local-Global Temporal Difference Learning for Satellite Video
Super-Resolution [55.69322525367221]
We propose to exploit the well-defined temporal difference for efficient and effective temporal compensation.
To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies.
Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches.
arXiv Detail & Related papers (2023-04-10T07:04:40Z) - RFC-Net: Learning High Resolution Global Features for Medical Image
Segmentation on a Computational Budget [4.712700480142554]
We propose Receptive Field Chain Network (RFC-Net) that learns high resolution global features on a compressed computational space.
Our experiments demonstrate that RFC-Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation.
arXiv Detail & Related papers (2023-02-13T06:52:47Z) - DQnet: Cross-Model Detail Querying for Camouflaged Object Detection [54.82390534024954]
A convolutional neural network (CNN) for camouflaged object detection tends to activate local discriminative regions while ignoring complete object extent.
In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN.
In order to obtain feature maps that could activate full object extent, a novel framework termed Cross-Model Detail Querying network (DQnet) is proposed.
arXiv Detail & Related papers (2022-12-16T06:23:58Z) - TINC: Tree-structured Implicit Neural Compression [30.26398911800582]
Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters.
Preliminary studies can only exploit either global or local correlation in the target data.
We propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions.
arXiv Detail & Related papers (2022-11-12T15:39:07Z) - Domain Adaptive Object Detection via Feature Separation and Alignment [11.4768983507572]
adversarial-based domain adaptive object detection (DAOD) methods have been developed rapidly.
We establish a Feature Separation and Alignment Network (FSANet) which consists of a gray-scale feature separation (GSFS) module, a local-global feature alignment (LGFA) module and a region-instance-level alignment (RILA) module.
Our FSANet achieves better performance on the target domain detection and surpasses the state-of-the-art methods.
arXiv Detail & Related papers (2020-12-16T01:44:34Z) - LRC-Net: Learning Discriminative Features on Point Clouds by Encoding
Local Region Contexts [65.79931333193016]
We present a novel Local-Region-Context Network (LRC-Net) to learn discriminative features on point clouds.
LRC-Net encodes fine-grained contexts inside and among local regions simultaneously.
Results show LRC-Net is competitive with state-of-the-art methods in shape classification and shape segmentation applications.
arXiv Detail & Related papers (2020-03-18T14:34:08Z) - Dense Residual Network: Enhancing Global Dense Feature Flow for
Character Recognition [75.4027660840568]
This paper explores how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers.
Technically, we propose an efficient and effective CNN framework, i.e., Fast Dense Residual Network (FDRN) for text recognition.
arXiv Detail & Related papers (2020-01-23T06:55:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.