Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
Personalq: Select, quantize, and serve personalized diffusion models for efficient inference
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A new benchmark shows current vision-language models can name foods but fail at estimating portion and nutrient values and often give unsafe dietary advice for chronic-disease patients.
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.
citing papers explorer
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Rethinking Event-Based Object Dtection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning
Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
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OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
A new benchmark shows current vision-language models can name foods but fail at estimating portion and nutrient values and often give unsafe dietary advice for chronic-disease patients.
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CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.