📡 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.

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.

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.

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.

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.

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.