OR3 converts OR clips to action-driven digital twins, uses LLM imagination for hypothetical ActDTs, and achieves 57.6 R@1 and 77.3 R@5 on 276 implicit queries from 386 robotic knee procedure clips, outperforming baselines.
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Contrastive learning with hard negative samples
18 Pith papers cite this work, alongside 37 external citations. Polarity classification is still indexing.
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Chameleon proposes the first large-scale cross-domain compositing dataset and a disentangled encoder plus gated diffusion transformer that outperforms prior in-domain and cross-domain methods on plausibility and fidelity.
ContrastAD achieves highest mean F1 on all five MTS benchmarks and highest AUC on three by building DTW-based sparse graph snapshots and contrasting divergent pairs with a stable anchor instead of enforcing invariance.
Contrastive Message Passing lets GNNs apply similarity-preserving transforms to positive edges and dissimilarity-inducing transforms to negative edges via soft positive semidefinite constraints on weights, yielding gains in low-label high-homophily regimes.
MASS-DPO derives a Plackett-Luce-specific log-determinant Fisher information objective to select non-redundant negative samples, matching or exceeding multi-negative DPO performance with substantially fewer negatives across four benchmarks and three model families.
DiffusionPrint learns robust forensic feature maps via MoCo-style contrastive training on diffusion inpainting fingerprints, boosting localization accuracy by up to 28% when fused into existing IFL systems and generalizing to unseen models.
Diffusion-based per-view harmonization for lighting-consistent object transfer between 3DGS scenes, using heterogeneous training data and final 3D consolidation.
Sampling pairs directly with auxiliary information for higher inclusion probabilities on informative pairs yields near-full pairwise loss performance at reduced computational cost.
CERA fine-tunes a dense retriever with triplet contrastive learning plus attention alignment to human rationales, claiming better retrieval effectiveness and faithfulness on clinical trial reports than Contriever and standard hard-negative baselines.
HOLA introduces multi-view multi-text alignment and a decoupled contrastive loss for state-of-the-art open-vocabulary 3D recognition on long-tail benchmarks.
AppRay integrates LLM-guided task-oriented exploration with a contrastive learning multi-label classifier and rule-based refiner to detect intra- and inter-page dark patterns, reporting 0.89/0.85 F1 on new datasets with large gains over prior methods.
ImpSH improves cross-domain generalization in implicit hate speech classification by aligning posts with implied statements and applying context-bounded semi-hard negative mining within a triplet learning setup.
A new synthetic dataset and geometry-consistent dense correspondence framework improve RGB-only pose estimation accuracy for surgical instruments on three evaluation datasets.
MSAlign aligns frozen DreaMS and ChemBERTa models with MLPs and candidate-based contrastive learning to outperform prior methods on molecule retrieval from MS/MS spectra while quantifying distribution shift in data splits.
SSA-ME uses saliency-aware modeling to reduce visual neglect and semantic drift, achieving SOTA results on the MMEB benchmark for multimodal retrieval.
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.
LoRA-adapted SAM 3 with hard-negative mining and phase-coherent filtering achieves median Dice 0.968 on pulmonary structures from 4DCT using seven annotated volumes.
Cosine similarity in SupCon with a delayed negative queue on wav2vec2 XLS-R yields the lowest equal error rates for deepfake audio detection on in-the-wild and pooled evaluations.
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Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.