Jingdong Wang, Sen Yang, Wankou Yang, Jiedong Zhuang, Ming Dai, Boyuan Tong, Boqiang Duan, Keyframe Mask
We lifted 12 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| Dmmm1997/VideoSEG-O3 | canonical | 12 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| dice_loss | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/trainer/sam_loss.py code served (permissive licence) · get_code("27e0ec37f99eb303") |
| format_reward | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards.py code served (permissive licence) · get_code("09e6ed2ece2264f7") |
| get_mask_from_json | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/dataset.py code served (permissive licence) · get_code("555397b8c8e37d9b") |
| iou_loss | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/trainer/sam_loss.py code served (permissive licence) · get_code("e40a43378d8a1fb1") |
| lr_keyframe_vs_mask_reward_dense | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards_dense.py code served (permissive licence) · get_code("f00c0b416be367d1") |
| lr_progressive_keyframe_reward_dense | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards_dense.py code served (permissive licence) · get_code("8e18f662aafb6741") |
| lr_vtg_iou_reward_dense | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards_dense.py code served (permissive licence) · get_code("ce95cb2555ca57ce") |
| resize_longest | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/dataset.py code served (permissive licence) · get_code("f198b4c1897d05e3") |
| resize_shortest | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/dataset.py code served (permissive licence) · get_code("a95077549e0188f3") |
| sigmoid_bce_loss | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/trainer/sam_loss.py code served (permissive licence) · get_code("96a68d44c9a31965") |
| think_format_reward | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards.py code served (permissive licence) · get_code("3bfb2a61622e10cb") |
| think_select_format_reward | Ran | Dmmm1997/VideoSEG-O3/projects/open_r1/rewards.py code served (permissive licence) · get_code("fd576996738fc5b8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Reasoning Video Object Segmentation (RVOS) demands a sophisticated integration of temporal dynamics, spatial details, and linguistic reasoning to achieve precise pixel-level localization. Existing methods are limited to reasoning over fixed initial inputs and lack the capacity to actively acquire further visual evidence, which is often essential for resolving complex references in long or intricate videos. To address this, we propose VideoSEG-O3, the first multi-turn reinforcement learning framework for RVOS that emulates the human "coarse-to-fine" cognitive process. It employs a multi-turn temporal-spatial chain-of-thought to capture fine-grained details by iteratively pinpointing critical intervals and keyframes. Additionally, to enable the policy to perceive segmentation quality beyond mere text probability of [SEG] during the RL stage, we introduce SEG-aware logit calibration, which integrates pixel-wise segmentation feedback directly into the token-level logits. Furthermore, we design a decoupled thinking trace to hierarchically decompose the reasoning process into temporal, spatial, and linguistic dimensions, and construct VTS-CoT, a specialized cold-start dataset featuring comprehensive reasoning trajectories. The code and models will be released at https:// github.com/Dmmm1997/VideoSEG-O3.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.06819")
get_code_for_paper("2606.06819")
have("2606.06819")
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