We lifted 7 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| upeee/GOO-GAZE2021 | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| boxes2centers | Ran | upeee/GOO-GAZE2021/gazefollowing/inference.py code served (permissive licence) · get_code("31715250d7d4d8c3") |
| calculate_metrics | Ran | upeee/GOO-GAZE2021/gazefollowing/evaluate_chong.py code served (permissive licence) · get_code("5ae5ac5e4843dd4e") |
| conv3x3 | Ran | upeee/GOO-GAZE2021/gazefollowing/models/resnet.py code served (permissive licence) · get_code("fac5364e2f53c6db") |
| resnet18 | Ran | upeee/GOO-GAZE2021/gazefollowing/models/resnet.py code served (permissive licence) · get_code("bbdb72e0282625a2") |
| resnet34 | Ran | upeee/GOO-GAZE2021/gazefollowing/models/resnet.py code served (permissive licence) · get_code("fc3bb9ff62a548e5") |
| select_nearest_bbox | Ran | upeee/GOO-GAZE2021/gazefollowing/inference.py code served (permissive licence) · get_code("a7807ca90843a348") |
| demo_random | Not yet run | upeee/GOO-GAZE2021/gazefollowing/inference.py code served (permissive licence) · get_code("32a45d12e3cc8edd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate only the pixel being looked at, and not the boundaries of a specific object of interest. This lack of object annotation presents an opportunity for further advancing gaze estimation research. To this end, we present a challenging new task called gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. To train and evaluate gaze networks on this task, we present the Gaze On Objects (GOO) dataset. GOO is composed of a large set of synthetic images (GOO Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment. Our work establishes extensive baselines on GOO by re-implementing and evaluating selected state-of-the art models on the task of gaze following and domain adaptation. Code is available on github.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.10793")
get_code_for_paper("2105.10793")
have("2105.10793")
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