Open Problems

Person Re-Identification & Face Recognition

Cross-Distribution Robustness and Granularity Evaluation in Cloth-Changing Person Re-Identification

Effect to explainOpen
Possible candidate · 3/5 runs5 papers report this40% from 2025+

Generated automatically from the limitations stated in 5 papers (CVPR, NeurIPS), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Existing cloth-changing person re-identification (CC-ReID) and gait recognition models are trained and tested on datasets with restricted clothing variation profiles, such as upper-body-only changes or invariant footwear. Models trained on these biased distributions experience severe performance drops (such as single-digit Rank-1 accuracy) when evaluated on full clothing or pants-only changes. Without a standardized cross-setting evaluation that separates upper, lower, full, and footwear changes, reported benchmark metrics obscure localized overfitting and fail to measure true clothing-invariant representations.

Why it matters

Establishes a rigorous diagnostic framework to identify which feature representations achieve true clothing invariance rather than exploiting static regional artifacts like unchanged footwear.

Ways to approach it

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  1. 1

    Multi-partition benchmark evaluation: Re-categorize test splits across existing benchmarks (e.g., CCPG, LTCC, PRCC) into distinct clothing-change partitions (upper-only, pants-only, full-change, shoe-masked vs. unmasked) to measure Rank-1 and mAP across leading RGB, gait, and disentanglement methods.

  2. 2

    Cross-dataset distribution shift study: Train representative CC-ReID models on datasets with varying clothing change distributions (e.g., SUSTech1K, CASIA-B*, and CCGR_MINI) and evaluate zero-shot cross-dataset generalization across varying clothing change granularities.

  3. 3

    Supervision sensitivity profiling: Measure the performance degradation of label-free/unsupervised, text-guided, and explicitly annotated multi-branch methods across varying ratios of clothing-change training pairs.

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Why it might fail

If trivial random masking augmentations during training completely resolve the cross-partition degradation across all existing architectures, rendering a dedicated robustness benchmark redundant.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

Person Re-Identification & Face Recognition

Effect to explainOpen

Robust Cross-Modality and Cross-Platform Retrieval for Person and Vehicle Re-Identification

Current cross-modality re-identification methods report relative improvements on benchmark datasets, yet their absolute retrieval performance drops severely in strongly mismatched sensor and platform scenarios (e.g., 9–16 mAP on RGBNT201 and MSVR310). When spectral shifts (RGB to Near-Infrared or Thermal) coincide with cross-platform viewpoint or domain shifts, identity discrimination fails at a fundamental level. This severe degradation blocks the practical deployment of automated re-identification systems across heterogeneous, multi-sensor surveillance networks operating in varying lighting and platform conditions.

Strong candidate · 4/5 runs3 papers report this100% from 2025+

Person Re-Identification & Face Recognition

Barrier to removeOpen

Cross-Encoder and Target-Free Robustness Benchmarking for Facial Representations

Current facial representation and transfer methods rely on training-time preconditions that frequently fail in real-world deployment. Specifically, techniques depend on collecting unlabeled target-domain demographic data—which is often legally or ethically prohibited—or evaluate strictly against known, seen feature extractors within face-only datasets. When the deployed encoder is changed or target data cannot be sampled in advance, systems experience substantial performance drops that remain unquantified across modern architectures. Without a unified benchmark testing methods under zero-target-data and unseen-encoder constraints, practitioners cannot determine which representation strategies legitimately transfer.

Possible candidate · 2/5 runs3 papers report this33% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.