LeVLJEPA is the first non-contrastive vision-language pretraining method that learns via cross-modal prediction without negatives, producing stronger dense features than contrastive baselines on VQA and segmentation tasks.
Noise or signal: The role of image backgrounds in object recognition
6 Pith papers cite this work. Polarity classification is still indexing.
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Visual encoders leak identity information; a one-shot linear subspace removal method (ISP) reduces leakage to near-chance levels while retaining high non-biometric utility across datasets.
Characterizes spurious correlation mechanisms in preference optimization via mean spurious bias and causal-spurious correlation leakage, demonstrates irreducible vulnerability to distribution shift, and introduces tie training as selective mitigation with validation on log-linear models and empirica
R3G improves vision-centric VQA by generating a reasoning plan before retrieval and reranking candidate images with an MLLM judge on relevance, target match, and answerability.
COinCO is a new dataset of inpainted COCO images with in- and out-of-context objects, enabling context reasoning, object prediction from scenes, and improved fake image detection.
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.
citing papers explorer
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LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives
LeVLJEPA is the first non-contrastive vision-language pretraining method that learns via cross-modal prediction without negatives, producing stronger dense features than contrastive baselines on VQA and segmentation tasks.
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From Measurement to Mitigation: Quantifying and Reducing Identity Leakage in Image Representation Encoders with Linear Subspace Removal
Visual encoders leak identity information; a one-shot linear subspace removal method (ISP) reduces leakage to near-chance levels while retaining high non-biometric utility across datasets.
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Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training
Characterizes spurious correlation mechanisms in preference optimization via mean spurious bias and causal-spurious correlation leakage, demonstrates irreducible vulnerability to distribution shift, and introduces tie training as selective mitigation with validation on log-linear models and empirica
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R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation
R3G improves vision-centric VQA by generating a reasoning plan before retrieval and reranking candidate images with an MLLM judge on relevance, target match, and answerability.
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Common Inpainted Objects In-N-Out of Context
COinCO is a new dataset of inpainted COCO images with in- and out-of-context objects, enabling context reasoning, object prediction from scenes, and improved fake image detection.
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Training-Free Object-Background Compositional T2I via Dynamic Spatial Guidance and Multi-Path Pruning
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.