PHOEBI is a benchmark dataset and LCO evaluation protocol for open-world multi-label bacterial species identification from phase-contrast microscopy of polymicrobial samples.
Query2label: A simple transformer way to multi-label classification
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Introduces the ELDOR UAV dataset and four benchmark tasks for semantic segmentation and classification of mining disturbances and ecological recovery in rainforest imagery.
DFPL introduces prototype-based disentanglement and alignment modules to preserve fine-grained consistency across heterogeneous modalities for better performance under missing data conditions.
CXR-LT 2026 introduces a radiologist-annotated multi-center dataset of 145k+ CXRs to benchmark robust multi-label classification on known classes and open-world generalization to unseen rare diseases.
A 241M multi-task student trained with suffix identity, VAR loss, and a decoupled Q2L head matches or beats most VLMs and safety APIs on grounded sensitive scene graphs at 7.6× lower latency.
Proposes cross-attention audio-video fusion and VE-MD latent-space models for group emotion recognition that avoid individual cues and report competitive performance via ablation studies on synthetic and real data.
The paper summarizes results from the SurgToolLoc and SurgVU challenges held at MICCAI conferences from 2022 to 2025.
citing papers explorer
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PHOEBI: An Open-World Benchmark for Bacterial Identification in Phase-Contrast Microscopy
PHOEBI is a benchmark dataset and LCO evaluation protocol for open-world multi-label bacterial species identification from phase-contrast microscopy of polymicrobial samples.
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ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest
Introduces the ELDOR UAV dataset and four benchmark tasks for semantic segmentation and classification of mining disturbances and ecological recovery in rainforest imagery.
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Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification
DFPL introduces prototype-based disentanglement and alignment modules to preserve fine-grained consistency across heterogeneous modalities for better performance under missing data conditions.
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CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
CXR-LT 2026 introduces a radiologist-annotated multi-center dataset of 145k+ CXRs to benchmark robust multi-label classification on known classes and open-world generalization to unseen rare diseases.
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SenBen: Sensitive Scene Graphs for Explainable Content Moderation
A 241M multi-task student trained with suffix identity, VAR loss, and a decoupled Q2L head matches or beats most VLMs and safety APIs on grounded sensitive scene graphs at 7.6× lower latency.
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Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
Proposes cross-attention audio-video fusion and VE-MD latent-space models for group emotion recognition that avoid individual cues and report competitive performance via ablation studies on synthetic and real data.
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Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
The paper summarizes results from the SurgToolLoc and SurgVU challenges held at MICCAI conferences from 2022 to 2025.