📡 Signal & Communications

🎞️ ECCV2026 · 7 paper notes

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

🔥 Top topics: Compression ×3

FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography

FreqPhys introduces a cardiac frequency-band constraint, learnable spectral modulation, and in-band noise selection into every conditional diffusion denoising step, then recovers pulse waveforms through cross-domain attention, demonstrating robustness across six datasets with a BUAA heart-rate RMSE of 1.31 bpm, although it does not lead every metric on every dataset.

MixCompress: Mixture of Experts for Variable Rate Learned Image Compression

Addressing severe gradient conflict between high and low bit-rates in dense variable-rate codecs, MixCompress integrates rate-conditioned Mixture-of-Experts (MoE) and Mixture-of-Depths (MoD) with Conditional Auxiliary Transforms (CAT), surpassing individually optimized single-rate models under a single unified checkpoint.

Raw-JPEG Adapter: Efficient Raw Image Compression with JPEG

To tackle the massive storage footprint of linear raw sensor images and their incompatibility with standard 8-bit JPEG pipelines, this paper introduces Raw-JPEG Adapter—a lightweight, learnable, and invertible pre-processing pipeline that predicts channel lookup tables, frequency DCT scaling, and spatial gamma maps embedded into the JPEG comment segment (<64 KB), enabling high-fidelity raw recovery with negligible decoding runtime.

Incentive Noise and Structural Prior Infusion for Multi-Modal Object Re-Identification

Addressing textual noise and cross-spectral structural discrepancies in multi-modal ReID, this work reformulates ambiguous language descriptions into task-aware positive-incentive noise to modulate visual global tokens, combining DINOv3 structural prompt priors with context-guided sparse fusion to achieve superior retrieval robustness under adverse conditions.

MaterialFlow: Attribute-Disentangled Material Transfer via Trajectory-Aware Velocity Modulation

To eliminate structural distortion and trajectory drift caused by feature entanglement and inversion accumulation in material transfer, MaterialFlow introduces an inversion-free trajectory-aware velocity modulation framework alongside a tri-attribute disentanglement paradigm for color, texture, and structural patterns on pre-trained Rectified Flow models.

Pol-CACTI: A System and dataset forHigh-Speed Polarized Video Compressive Imaging

To overcome the frame-rate bottleneck of conventional DoFP sensors and prevent severe error accumulation in decoupled two-stage pipelines, Pol-CACTI introduces a hardware-software co-designed polarization video SCI prototype, a curated 8,889-frame dynamic polarized video dataset, and a physics-driven joint reconstruction network featuring a Polarization-Aware Learnable Back-Projection (PA-LBP) module to achieve high-fidelity high-speed polarized video from a single compressive snapshot.

Prototype-Conditioned Imagination for Compositional Zero-Shot Learning

To tackle ambiguous cross-modal primitive alignment and the lack of explicit attribute-object interaction in Compositional Zero-Shot Learning (CZSL), this paper proposes Prototype-Conditioned Imagination (PCI), a two-stage framework that couples optimal-transport-regularized bidirectional disentanglement with prototype-driven affine modulation to synthesize virtual compositional representations, establishing new state-of-the-art results across four benchmarks under both closed-world and open-world settings.