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cs.CV2024
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks
Jiacheng Chen, Tianhao Liang, Sherman Siu +13
We present MEGA-Bench, an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users. Our obj…
cs.CV2024★ 1 cited
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Kai Zhang, Yi Luan, Hexiang Hu +5
Image retrieval, i.e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-ba…
cs.CV2023★ 2 cited
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Cong Wei, Yang Chen, Haonan Chen +5
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text description…