L-CIRR is a benchmark for composed image retrieval (CIR) with long-form modification texts. It extends the CIRR dataset by replacing each short, single-sentence modification text with a long, detailed, multi-sentence description generated by a vision-language model (Gemini-2.5-Flash), while preserving the original image pairs and benchmark protocol. This dataset accompanies the paper "Beyond Single Sentences: Composed Image Retrieval with Long-Form Modification Texts" (ACCV 2026). Existing CIR benchmarks provide only brief, often ambiguous modification texts that capture a small subset of the visual differences between the reference and target images, giving weak supervision for learning…
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Junyeong Jang
JunYeong825
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