OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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RCSR is a personalization-friendly federated framework that improves cross-modal retrieval accuracy and stability under missing modalities via semantic routing and adapters.
Using a structured rewrite instead of CoT as the generative interface improves MLLM-based multimodal embedding performance while cutting thinking tokens by about half.
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
A framework integrates MM-LLMs into recommendation systems via caption generation as categorical features, reporting 0.35% offline AUC lift and 0.02% online metric improvement.
Reproducibility study finds TRIANGLE yields up to +8.7 Recall@1 gains in zero-shot multimodal retrieval but fails to reproduce learning-from-scratch results owing to joint optimization instability with DTM loss.
citing papers explorer
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OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization
RCSR is a personalization-friendly federated framework that improves cross-modal retrieval accuracy and stability under missing modalities via semantic routing and adapters.
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Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings
Using a structured rewrite instead of CoT as the generative interface improves MLLM-based multimodal embedding performance while cutting thinking tokens by about half.
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WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
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A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
A framework integrates MM-LLMs into recommendation systems via caption generation as categorical features, reporting 0.35% offline AUC lift and 0.02% online metric improvement.
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RE-TRIANGLE: Does TRIANGLE Enable Multimodal Alignment Beyond Cosine Similarity in Retrieval?
Reproducibility study finds TRIANGLE yields up to +8.7 Recall@1 gains in zero-shot multimodal retrieval but fails to reproduce learning-from-scratch results owing to joint optimization instability with DTM loss.
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