Lifelong person re-identification (LReID) is developed for dynamic domains where domain distribution is constantly changing due to climate changes, scene changes, etc., and the data can only be collected for a specific scenario over a period of time. With the development of ReID, the issue of clothing changes has also attracted attention. Clothing change itself should be solved more from the perspective of lifelong learning because pedestrians may wear new clothes and the time span of their appearance can be long which can also cause domain changes. Meanwhile, it is difficult to know in advance whether a pedestrian is cloth-changing or cloth-consistent. However, current LReID tasks overlook these issues. To overcome these limitations, we introduce a more practical LReID task, denoted as L4C-ReID (Lifelong Person Re-Identification in Cloth-Changing and Cloth-Consistent Scenarios). This novel task empowers ReID models capable of adapting to incrementally encountered cloth-changing and cloth-consistent domains without prior knowledge of the scenario type and generalizing to unseen domains. A key challenge supposed to be fixed for LReID is the stability-plasticity dilemma. Unlike current LReID methods, which implement plasticity and stability by two contradictory loss items to achieve a sub-optimal balance, we propose an effective scheme termed Unified Stability and Plasticity (USP) that unifies these seemingly disparate concepts to achieve both harmoniously. Taking inspiration from the cognitive processes in the human brain, we decompose the cognitive processes into two independent processes: knowledge representation and knowledge operation. We then design a Knowledge Representation and Operation (KRO) framework to represent and operate the knowledge like the human brain which can better learn new knowledge and consolidate old knowledge to coordinate plasticity and stability. Additionally, we introduce Plasticizing with Stability (PWS) to generalize and optimize the learned knowledge, which integrates the implementation of plasticity and stability into one common objective item to achieve both simultaneously. To simulate the L4C-ReID setup, we gather existing cloth-changing and cloth-consistent datasets to provide a new benchmark. Extensive experiments conducted both on this new benchmark and previous benchmarks established for previous LReID setup, demonstrate the superiority of our method.

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.

Web accessibility evaluation examines how well websites comply with accessibility guidelines which help people with disabilities to perceive, navigate and contribute to the Web. This demanding task usually requires manual assessment by experts with many years of training and experience. However, not enough experts are available to carry out the increasing number of evaluation projects while non-experts often have different opinions about the presence of accessibility barriers. Addressing these issues, we introduce a crowdsourcing system with a novel truth inference algorithm to derive reliable and accurate assessments from conflicting opinions of evaluators. Extensive evaluation on 23,901 complex tasks assessed by 50 people with and without disabilities shows that our approach outperforms state of the art approaches. In addition, we conducted surveys to identify frequent barriers that people with disabilities are facing in their daily lives and the difficulty to access Web pages when they encounter these barriers. The frequencies and severities of barriers correlate with their derived importance in our evaluation project.