Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID

TL;DR

This paper introduces RLQ, a novel framework combining coarse attribute prediction and task-agnostic distillation to improve clothes-changing person re-identification in low-quality real-world images, outperforming existing methods.

Contribution

The proposed RLQ framework uniquely integrates coarse attribute prediction and task-agnostic distillation to enhance robustness against low-quality images in clothes-changing Re-ID.

Findings

RLQ improves Top-1 accuracy by 1.6%-2.9% on real-world datasets.

RLQ shows a 5.3%-6% Top-1 improvement on PRCC.

RLQ maintains competitive performance on LTCC.

Abstract

This work focuses on Clothes Changing Re-IDentification (CC-ReID) for the real world. Existing works perform well with high-quality (HQ) images, but struggle with low-quality (LQ) where we can have artifacts like pixelation, out-of-focus blur, and motion blur. These artifacts introduce noise to not only external biometric attributes (e.g. pose, body shape, etc.) but also corrupt the model's internal feature representation. Models usually cluster LQ image features together, making it difficult to distinguish between them, leading to incorrect matches. We propose a novel framework Robustness against Low-Quality (RLQ) to improve CC-ReID model on real-world data. RLQ relies on Coarse Attributes Prediction (CAP) and Task Agnostic Distillation (TAD) operating in alternate steps in a novel training mechanism. CAP enriches the model with external fine-grained attributes via coarse predictions, thereby reducing the effect of noisy inputs. On the other hand, TAD enhances the model's internal feature representation by bridging the gap between HQ and LQ features, via an external dataset through task-agnostic self-supervision and distillation. RLQ outperforms the existing approaches by 1.6%-2.9% Top-1 on real-world datasets like LaST, and DeepChange, while showing consistent improvement of 5.3%-6% Top-1 on PRCC with competitive performance on LTCC. *The code will be made public soon.*