👥 Social Computing

🎞️ ECCV2026 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (3) · 🔬 ICLR2026 (17) · 💬 ACL2026 (44) · 🧪 ICML2026 (9) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (20)

🔥 Top topics: Self-Supervised Learning ×2

Dual-Margin Embedding for Fine-Grained Long-Tailed Plant Taxonomy

Addressing the co-occurring challenges of fine-grained morphological ambiguity, extreme long-tailed taxa distribution, and open-world domain shifts, this paper proposes TaxoNet, which couples a mathematically grounded dual-margin objective to suppress repulsive head-class gradients on rare prototypes with an adaptive norm-guided sample selection strategy.

Evidence Triangulation for Multimodal Fact-Checking in the Wild

Addressing the lack of in-the-wild training benchmarks and the structural limitations of existing fact-checking models, this paper introduces X-POSE, a real-world crowdsourced benchmark derived from X Community Notes, alongside TRENT, a lightweight architecture that triangulates image, text, and external evidence via three dedicated cross-attention streams and relational NLI fusion.

Fourier Self-Supervision for Fine-Grained Generalized Category Discovery

To overcome the failure of standard contrastive learning in fine-grained Generalized Category Discovery caused by subtle inter-class differences, this paper introduces Fourier Self-Supervision that decouples the latent space into low- and high-frequency sub-dimensions based on adaptive SNR thresholds, harnessing low frequencies for abstract hierarchical discovery and high frequencies for discriminative fine details.

HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding

Targeting the critical cognitive gap where Large Vision-Language Models (LVLMs) recognize characters but fail to perceive brushwork dynamics, HCSU introduces the first large-scale benchmark that decouples authentic ink manuscripts (Tie) from stone rubbings (Bei) across 39,307 character images from 49 historical masters, incorporating hierarchical aesthetic descriptions to enable dual evaluation on 8-way style discrimination and interpretable aesthetic reasoning.

Coarse-to-fine Contrast: A Hybrid Self-supervised Method for Non-rigid 3D Shape Matching

This paper introduces a coarse-to-fine hybrid self-supervised framework for non-rigid 3D shape matching that enforces consistency between coarse soft maps and multi-scale refined hard correspondences across symmetric Laplacian and elastic basis branches, eliminating computationally expensive least-squares solvers and test-time fine-tuning while achieving state-of-the-art accuracy.

MED-LCDS: Multi-Expert-Domain CLIP Classification via Logit Calibration

To resolve cross-domain interference and artificial overconfidence caused by heterogeneous logit scales when merging independently trained LoRA experts into a unified vision-language model, MED-DSLC (MED-LCDS) combines domain-supervised mixture-of-experts routing with learnable domain-wise temperature calibration, effectively restoring global logit comparability across unified label spaces.

PanoRec: Spatially-Structured Sequence Modeling for Multi-Granularity Panoramic Retrieval

Addressing the severe semantic dilution caused by compressing 360-degree scenes into a single global embedding in conventional vision-language models, PanoRec serializes distortion-free cubemap faces, a downsampled global panorama, and spatial anchor tokens into a unified sequence, combining dynamic MaxSim routing with a joint spatial InfoNCE objective to achieve efficient multi-granularity panoramic retrieval in a single forward pass.