We lifted 14 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| congvvc/instructseg | canonical | 10 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| window_partition | Ran | congvvc/instructseg/instructseg/model/mask_encoder/swin_trans.py code served (permissive licence) · get_code("144d10b49baeb8a6") |
| FeedForward | Ran | congvvc/instructseg/instructseg/model/language_model/ovp.py code served (permissive licence) · get_code("4d75570b4e91d0b4") |
| exists | Ran | congvvc/instructseg/instructseg/model/language_model/ovp.py code served (permissive licence) · get_code("aa5486a3650902d8") |
| expand2square | Ran | congvvc/instructseg/instructseg/model/mipha/mm_utils.py code served (permissive licence) · get_code("592b3c1a88f93d7c") |
| find_linear_layers | Ran | congvvc/instructseg/instructseg/train/merge_lora_weights_and_save_hf_model.py code served (permissive licence) · get_code("520d771d01a3c5ef") |
| get_mask_from_json | Ran | congvvc/instructseg/instructseg/model/datasets_mapper/IVS_mapper.py code served (permissive licence) · get_code("231df0d16f40deaf") |
| load_image_from_base64 | Ran | congvvc/instructseg/instructseg/model/mipha/mm_utils.py code served (permissive licence) · get_code("c3ee9d07c900dd55") |
| load_revos_json | Ran | congvvc/instructseg/instructseg/datasets/ytvos.py code served (permissive licence) · get_code("50b4f58ac51e3844") |
| parse_args | Ran | congvvc/instructseg/instructseg/train/merge_lora_weights_and_save_hf_model.py code served (permissive licence) · get_code("9e11de9f538299ab") |
| window_reverse | Ran | congvvc/instructseg/instructseg/model/mask_encoder/swin_trans.py code served (permissive licence) · get_code("61bf152e6a42a184") |
| build_swin_t | Not yet run | congvvc/instructseg/instructseg/model/mask_encoder/swin_trans.py code served (permissive licence) · get_code("670eda77b277eb8a") |
| check_shape | Not yet run | congvvc/instructseg/instructseg/model/language_model/einops_exts.py code served (permissive licence) · get_code("bbe128f6633ed802") |
| filter_empty_instances_soft | Not yet run | congvvc/instructseg/instructseg/datasets/ytvos.py code served (permissive licence) · get_code("ee5e474262386435") |
| process_images | Not yet run | congvvc/instructseg/instructseg/model/mipha/mm_utils.py code served (permissive licence) · get_code("344dff4791fd1381") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two areas. In this paper, we define the union of referring segmentation and reasoning segmentation at both the image and video levels as Instructed Visual Segmentation (IVS). Correspondingly, we propose InstructSeg, an end-to-end segmentation pipeline equipped with MLLMs for IVS. Specifically, we employ an object-aware video perceiver to extract temporal and object information from reference frames, facilitating comprehensive video understanding. Additionally, we introduce vision-guided multi-granularity text fusion to better integrate global and detailed text information with fine-grained visual guidance. By leveraging multi-task and end-to-end training, InstructSeg demonstrates superior performance across diverse image and video segmentation tasks, surpassing both segmentation specialists and MLLM-based methods with a single model. Our code is available at https://github.com/congvvc/InstructSeg.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2412.14006")
get_code_for_paper("2412.14006")
have("2412.14006")
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