Papers for

retail analytics teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Person re-identification improves with clothing change awareness

SCORE: SubDistribution-aware Collaborative Knowledge Reinforcing for Cloth-Hybrid Lifelong Person Re-Identification

Abstract: Lifelong Person Re-Identification (LReID) aims to train a unified person retrieval model from a non-stationary data stream. Existing LReID methods mainly focus on scenarios where the clothing of each person is consistent. Recently, the Cloth-Hybrid LReID (CH-LReID) where cloth-consistent and cloth-changing data alternately occur, has emerged as a more practical and challenging scenario. Due to the conflict between clothing-relevant and clothing-irrelevant knowledge, the well-known catastrophic forgetting problem is significantly exacerbated in this task. To address this issue, we propose a SubDistribution-aware COllaborative Knowledge REinforcing (SCORE) framework, where our key idea is explicitly modeling the intra-identity diversity to continually consolidate distinct cloth-consistent and cloth-changing knowledge. Specifically, an Adaptive SubDistribution Modeling mechanism is developed, where a set of distributional subprototypes is assigned to each identity to capture the intra-identity diversity, improving the compatibility between cloth-consistent and cloth-changing knowledge. Then, a Distributional Knowledge Reinforcement scheme is introduced, where the knowledge of old distributional subprototypes is retained in the new ones by a collaborative aligning mechanism. Extensive experiments show that our SCORE achieves the state-of-the-art performance. Our code is available at https://github.com/zhoujiahuan1991/ECCV2026-SCORE

Fri 11 SeptComputer Vision and Pattern Recognition
The gist
Person re-identification means recognizing the same person across different images or video, even when they change clothes. This is a hard problem because the model can forget old information when learning new data, especially when clothing changes. The authors propose a method called SCORE that models different clothing styles for the same person separately and keeps old knowledge from being lost. This method helps the system better identify people despite these clothing changes and improves performance.
Open → 2609.12577v1

CrossLink links device IDs across networks to break privacy protections

CrossLink: Breaking Location Privacy by Linking Device Identifiers Across Protocols

Abstract: Smartphones simultaneously transmit temporary identifiers over LTE, WiFi, and BLE. Existing privacy defenses analyze identifier randomization per protocol, implicitly assuming that these protections compose across protocols. We show that they do not: Even when each protocol leaks only temporary identifiers and the adversary is fully passive, unsynchronized identifier rotations allow cross-protocol stitching of device traces. We present CrossLink, an uncertainty-aware tracing algorithm that links identifiers across time, space, and protocols under noisy localization and mobility. We evaluate CrossLink using controlled lab experiments with commodity devices and large-scale mobility simulation. Under large-scale mobility simulation, CrossLink reconstructs full traces for 83% of users, versus 22% for the best single-protocol baseline, showing that location privacy must be analyzed jointly across protocols. We further show that CrossLink remains effective under partial coverage: strategically placed sniffers near LTE handover regions, mobile sniffers, and limited high-coverage subregions retain sufficient cross-protocol evidence to bridge observation gaps, achieving substantially higher linkability than random deployments.

Wed 9 SeptCryptography and Security
The gist
Phones send temporary ID codes over different wireless networks like LTE, WiFi, and Bluetooth to protect your location privacy. The authors found that even if each network changes these codes regularly, a passive listener can still connect the dots across networks to track a phone's movements. They built CrossLink, a tool that carefully matches these changing IDs over time and across different signals, revealing how these protections fail when combined. Their tests show that CrossLink can successfully reconstruct the paths of most users, exposing location privacy risks.
Open → 2609.09963v1