ConceptPose delivers state-of-the-art zero-shot relative pose estimation by matching open-vocabulary 3D concept vectors derived from VLM saliency maps, beating the strongest baseline by 62% in ADD(-S) without training.
Learning transferable visual models from natural language supervi- sion
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MRCL extends pairwise spatial contrastive pre-training to multi-hop paths in scene graphs, yielding NDCG@5 = 0.748 on GQA graph retrieval and gains on spatial recognition and QA tasks.
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.
citing papers explorer
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ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
ConceptPose delivers state-of-the-art zero-shot relative pose estimation by matching open-vocabulary 3D concept vectors derived from VLM saliency maps, beating the strongest baseline by 62% in ADD(-S) without training.
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Multi-hop Relational Contrastive Learning: Extending Spatial Contrastive Pre-training Beyond Pairwise Relations
MRCL extends pairwise spatial contrastive pre-training to multi-hop paths in scene graphs, yielding NDCG@5 = 0.748 on GQA graph retrieval and gains on spatial recognition and QA tasks.
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CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
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G-MIXER: Geodesic Mixup-based Implicit Semantic Expansion and Explicit Semantic Re-ranking for Zero-Shot Composed Image Retrieval
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
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ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.