Abstract
Person re-identification faces two core challenges: precisely locating the foreground target while suppressing background noise and extracting fine-grained features from the target region. Numerous visual-only approaches address these issues by partitioning an image and applying attention modules, yet they rely on costly manual annotations and struggle with complex occlusions. Recent multimodal methods, motivated by CLIP, introduce semantic cues to guide visual understanding. However, they focus solely on foreground information, but overlook the potential value of background cues. Inspired by human perception, we argue that background semantics are as important as the foreground semantics in ReID, as humans tend to eliminate background distractions while focusing on target appearance. Therefore, this paper proposes an end-to-end framework that jointly models foreground and background information within a dual-branch cross-modal feature extraction pipeline. To help the network distinguish between the two domains, we propose an intra-semantic alignment and inter-semantic adversarial learning strategy. Specifically, we align visual and textual features that share the same semantics across domains, while simultaneously penalizing similarity between foreground and background features to enhance the network's discriminative power. This strategy drives the model to actively suppress noisy background regions and enhance attention toward identity-relevant foreground cues. Comprehensive experiments on two holistic and two occluded ReID benchmarks demonstrate the effectiveness and generality of the proposed method, with results that match or surpass those of current state-of-the-art approaches.
Abstract (translated)
人员再识别面临两大核心挑战：一是精确定位前景目标同时抑制背景噪声，二是从目标区域提取细微特征。众多仅依赖视觉的方法通过图像分割和应用注意力模块来解决这些问题，但它们依赖于昂贵的手动注释，并且在处理复杂遮挡时表现不佳。受CLIP方法启发的最近多模态方法引入了语义线索以指导视觉理解，然而这些方法仅仅关注前景信息，而忽略了背景线索的潜在价值。 受到人类感知机制的启发，我们认为，在再识别任务中，背景语义与前景语义同样重要。这是因为人在关注目标外观时往往会消除背景干扰。因此，本文提出了一种端到端框架，该框架在双分支跨模态特征提取管道内共同建模前景和背景信息。为了帮助网络区分这两个领域，我们提出了一种内部语义对齐和跨域语义对抗学习策略：具体而言，在不同领域的视觉与文本特征共享相同语义时进行对齐，同时惩罚前景与背景特征之间的相似度以增强网络的判别能力。这种策略促使模型积极抑制噪声背景区域并提高对身份相关的前景线索的关注。 在两个全景和两个遮挡再识别基准上的全面实验表明了所提出方法的有效性和通用性，并且其结果可匹敌或超越目前最先进的方法。
URL
https://arxiv.org/abs/2509.03032
