We lifted 4 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/vsc2022 | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| evaluate_matching_track | Not yet run | facebookresearch/vsc2022/vsc/metrics.py pointer only (licence: MIT) · get_code("79217acf4e24ca78") |
| format_video_id | Not yet run | facebookresearch/vsc2022/vsc/metrics.py pointer only (licence: MIT) · get_code("9a3f58145b0c92e5") |
| match_metric | Not yet run | facebookresearch/vsc2022/vsc/metrics.py pointer only (licence: MIT) · get_code("d367ee4a5060a1b2") |
| remove_l2_norm | Not yet run | facebookresearch/vsc2022/vsc/baseline/adapt_sscd_model.py pointer only (licence: MIT) · get_code("a962ad22eb594d9b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This work introduces a dataset, benchmark, and challenge for the problem of video copy detection and localization. The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video ("detection"), and additionally temporally localizing the shared content within each video ("localization"). The benchmark is designed to evaluate methods on these two tasks, and simulates a realistic needle-in-haystack setting, where the majority of both query and reference videos are "distractors" containing no copied content. We propose a metric that reflects both detection and localization accuracy. The associated challenge consists of two corresponding tracks, each with restrictions that reflect real-world settings. We provide implementation code for evaluation and baselines. We also analyze the results and methods of the top submissions to the challenge. The dataset, baseline methods and evaluation code is publicly available and will be discussed at a dedicated CVPR'23 workshop.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.09489")
get_code_for_paper("2306.09489")
have("2306.09489")
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