[Paper] S3-CLIP: Video Super Resolution for Person-ReID

Source: arXiv - 2601.08807v1

Overview

The paper presents S3‑CLIP, a novel framework that fuses video super‑resolution (VSR) with CLIP‑based person re‑identification (ReID). By first enhancing the visual quality of low‑resolution tracklets—especially those captured from aerial platforms—the authors show that downstream ReID performance can be significantly boosted, a crucial step for real‑world surveillance and search‑and‑rescue deployments.

Key Contributions

• First systematic study of VSR for person‑ReID: Demonstrates that improving raw video quality before feature extraction yields measurable gains.
• Task‑driven super‑resolution pipeline: Adapts state‑of‑the‑art VSR models (e.g., EDVR, BasicVSR++) to the specific needs of ReID, including temporal consistency and identity preservation.
• Integration with CLIP‑ReID: Leverages the powerful vision‑language encoder CLIP as the backbone for extracting robust, modality‑agnostic embeddings from super‑resolved frames.
• Competitive results on the VReID‑XFD benchmark: Achieves 37.52 % mAP (aerial→ground) and 29.16 % mAP (ground→aerial), with up to ~18 % absolute improvement in Rank‑10 for the hardest cross‑view scenario.
• Open‑source pipeline: The authors release code and pretrained models, facilitating reproducibility and further research.

Methodology

1. Video Super‑Resolution Front‑End

   • Input: Raw low‑resolution video tracklets (e.g., 240×135 from UAVs).
   • Architecture: A modern VSR network (EDVR‑style) that processes a short frame window (typically 5–7 frames) to exploit temporal redundancy.
   • Losses: Combination of pixel‑wise L1/L2 loss, perceptual loss (VGG‑based), and an identity‑preserving loss that penalizes changes in CLIP embeddings before and after upscaling.

2. CLIP‑Based ReID Backbone

   • Super‑resolved frames are fed into the frozen visual encoder of CLIP (ViT‑B/32).
   • A lightweight projection head maps CLIP embeddings to a ReID‑specific space, trained with a standard cross‑entropy + triplet loss on the labeled identities.

3. Training Strategy

   • Two‑stage training:
     1. Train the VSR module on a generic video SR dataset (e.g., REDS) with the identity loss added.
     2. Fine‑tune the ReID head on the VReID‑XFD training split while keeping the VSR weights frozen.
   • Temporal aggregation: During inference, frame‑level embeddings are averaged across the tracklet to produce a single robust descriptor per person.

4. Evaluation Protocol

   • Follow the VReID‑XFD benchmark’s cross‑view splits (aerial‑to‑ground and ground‑to‑aerial).
   • Report standard metrics: mean Average Precision (mAP) and Cumulative Matching Characteristic (Rank‑k).

Results & Findings

• The biggest jump appears in the ground‑to‑aerial direction, where low‑resolution aerial footage traditionally hurts ReID.
• Ablation studies confirm that removing the identity‑preserving loss reduces mAP by ~2 %, highlighting the importance of keeping person‑specific features intact during upscaling.
• Visual inspection shows sharper facial and clothing details after VSR, which directly translates to more discriminative CLIP embeddings.

Practical Implications

• Surveillance & Security: Operators can feed raw UAV footage into existing CLIP‑based ReID pipelines without needing expensive high‑resolution cameras; the VSR front‑end lifts the quality enough for reliable cross‑camera matching.
• Search‑and‑Rescue: In disaster zones, drones often capture low‑detail video; S3‑CLIP can improve the chance of locating missing persons across heterogeneous camera networks.
• Edge Deployment: The VSR module can run on modern AI accelerators (e.g., NVIDIA Jetson, Qualcomm Hexagon) at ~15 fps for 720p output, making it feasible for on‑device preprocessing before transmitting compact embeddings.
• Generalizable Pipeline: Because the ReID head relies on a frozen CLIP encoder, the same super‑resolution front‑end can be paired with other downstream tasks (e.g., action recognition, attribute classification) with minimal re‑training.

Limitations & Future Work

• Computation Overhead: Adding VSR increases inference latency and power consumption; real‑time constraints on low‑power edge devices remain a challenge.
• Domain Gap: The VSR model is pre‑trained on generic video datasets; performance may degrade on extreme weather or night‑time UAV footage.
• Identity Drift: Although the identity‑preserving loss mitigates it, subtle artifacts can still alter fine‑grained features (e.g., small logos).

Future Directions

• Explore lightweight VSR architectures (e.g., transformer‑lite) tailored for edge inference.
• Incorporate self‑supervised adaptation to fine‑tune the VSR module on unlabeled surveillance streams.
• Extend the framework to multi‑modal inputs (thermal + RGB) for robust ReID under adverse conditions.

Authors

• Tamas Endrei
• Gyorgy Cserey

Paper Information

• arXiv ID: 2601.08807v1
• Categories: cs.CV, cs.AI
• Published: January 13, 2026
• PDF: Download PDF