{"url":"/dataset/fcot","name":"FCoT","full_name":"Foreground Chain-of-Thought","description_markdown":"FCoT (Chain‑of‑Thought Segmentation) is replicate the step-by-step reasoning process a human annotator follows when using SAM2 to generate masks. Each example pairs an image with:\r\n\r\n- A bounding box locating the target object,\r\n\r\n- A sequence of foreground/background point prompts for refining the mask,\r\n\r\n- Natural language explanations (chain‑of‑thought) generated by Gemini‑2.5‑Pro summarizing the annotation process.","description_withheld":null,"homepage":"https://huggingface.co/datasets/geshang/FCoT","introduced_date":"2025-06-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/seg-r1-segmentation-can-be-surprisingly","title":"Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning","first_author":"Zuyao You","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Foreground Segmentation","url":"/task/foreground-segmentation","datasets_with_task":"/datasets/task/foreground-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FCoT"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}