📡 Signal & Communications

🎞️ ECCV2024 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (8) · 🧪 ICML2026 (2) · 🤖 AAAI2026 (3) · 🧠 NeurIPS2025 (5) · 📹 ICCV2025 (3)

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics

This paper constructs the Defect Spectrum dataset, providing fine-grained, semantic-rich, and large-scale multi-class defect annotations (125 defect classes, 3,518 + 1,920 images) across four industrial benchmarks. It also proposes Defect-Gen, a two-stage diffusion generator, to synthesize high-quality, diverse defect images under few-shot conditions, improving defect segmentation mIoU by up to 9.85.

Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging

This paper proposes extracting a compact Dual-Exposure Feature (DEF) from dual-exposure HDR image pairs, based on which two ultra-lightweight illuminant estimators, EMLP and ECCC, are constructed. They achieve or exceed the performance of prior methods requiring hundreds of thousands of parameters, while using only a few hundred to a few thousand parameters.

PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation

This paper proposes PYRA, which generates decoupled adaptive modulation weights in parallel and modulates features of tokens to be merged using a re-activation strategy. This approach enables Vision Transformers to achieve both training efficiency (tuning only 0.4% parameters) and inference efficiency (approx. 1.7x-3.2x speedup) during downstream task adaptation, achieving comparable or superior performance to uncompressed PEFT methods.

QueryCDR: Query-based Controllable Distortion Rectification Network for Fisheye Images

The proposed QueryCDR network, utilizing a distortion-aware learnable query mechanism (DLQM) and two controllable modulation modules (CCMB/CAMB), achieves high-quality controllable rectification for fisheye images with various distortion degrees without retraining for the first time.

RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images

This paper proposes RAW-Adapter, which efficiently adapts sRGB pre-trained models to camera RAW images with extremely small parameter overhead (0.2–0.8M) via an input-level adapter (learnable ISP stages) and a model-level adapter (injecting ISP intermediate features into the backbone). It achieves SOTA performance on detection and segmentation tasks under various lighting conditions, including normal, low-light, and overexposure.

Unsupervised Exposure Correction

This paper proposes the first unsupervised exposure correction (UEC) method, which leverages multi-exposure sequences generated freely by ISP pipelines to train images as mutual ground truths. It designs a pixel-level transformation function with only 19K parameters to preserve image details, outperforming supervised SOTA on exposure correction and downstream edge detection.