We lifted 15 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 |
|---|---|---|
| ttgeng233/UniAV | canonical | 10 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| Linear | Ran | ttgeng233/UniAV/libs/modeling/blocks.py code served (permissive licence) · get_code("10865bbb99140edd") |
| ctr_diou_loss_1d | Ran | ttgeng233/UniAV/libs/modeling/losses.py code served (permissive licence) · get_code("be1ca95e264f135e") |
| drop_path | Ran | ttgeng233/UniAV/libs/modeling/blocks.py code served (permissive licence) · get_code("a34c005ba2203f35") |
| get_sinusoid_encoding | Ran | ttgeng233/UniAV/libs/modeling/blocks.py code served (permissive licence) · get_code("3531df7b0c9b0791") |
| label_points | Ran | ttgeng233/UniAV/libs/datasets/data_utils.py code served (permissive licence) · get_code("91a965d8ba54bf18") |
| load_config | Ran | ttgeng233/UniAV/libs/core/config.py code served (permissive licence) · get_code("723915877968278d") |
| register_multimodal_backbone | Ran | ttgeng233/UniAV/libs/modeling/models.py code served (permissive licence) · get_code("e85f9ec07f7d587b") |
| register_multimodal_meta_arch | Ran | ttgeng233/UniAV/libs/modeling/models.py code served (permissive licence) · get_code("0fc8e74a8c092f28") |
| sigmoid_focal_loss | Ran | ttgeng233/UniAV/libs/modeling/losses.py code served (permissive licence) · get_code("54293ea22bd4f952") |
| truncate_feats | Ran | ttgeng233/UniAV/libs/datasets/data_utils.py code served (permissive licence) · get_code("65864282f76b5575") |
| make_dataset | Not yet run | ttgeng233/UniAV/libs/datasets/datasets.py code served (permissive licence) · get_code("ffebfbb8094c0dcc") |
| make_multimodal_backbone | Not yet run | ttgeng233/UniAV/libs/modeling/models.py code served (permissive licence) · get_code("80bc46d5a1acbb10") |
| register_dataset | Not yet run | ttgeng233/UniAV/libs/datasets/datasets.py code served (permissive licence) · get_code("2ee0df0825339a6e") |
| register_generator | Not yet run | ttgeng233/UniAV/libs/datasets/datasets.py code served (permissive licence) · get_code("3db1c5ab5f3f0198") |
| trivial_batch_collator | Not yet run | ttgeng233/UniAV/libs/datasets/data_utils.py code served (permissive licence) · get_code("0c250bf03b8c9311") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Video event localization tasks include temporal action localization (TAL), sound event detection (SED) and audio-visual event localization (AVEL). Existing methods tend to over-specialize on individual tasks, neglecting the equal importance of these different events for a complete understanding of video content. In this work, we aim to develop a unified framework to solve TAL, SED and AVEL tasks together to facilitate holistic video understanding. However, it is challenging since different tasks emphasize distinct event characteristics and there are substantial disparities in existing task-specific datasets (size/domain/duration). It leads to unsatisfactory results when applying a naive multi-task strategy. To tackle the problem, we introduce UniAV, a Unified Audio-Visual perception network to effectively learn and share mutually beneficial knowledge across tasks and modalities. Concretely, we propose a unified audio-visual encoder to derive generic representations from multiple temporal scales for videos from all tasks. Meanwhile, task-specific experts are designed to capture the unique knowledge specific to each task. Besides, instead of using separate prediction heads, we develop a novel unified language-aware classifier by utilizing semantic-aligned task prompts, enabling our model to flexibly localize various instances across tasks with an impressive open-set ability to localize novel categories. Extensive experiments demonstrate that UniAV, with its unified architecture, significantly outperforms both single-task models and the naive multi-task baseline across all three tasks. It achieves superior or on-par performances compared to the state-of-the-art task-specific methods on ActivityNet 1.3, DESED and UnAV-100 benchmarks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.03179")
get_code_for_paper("2404.03179")
have("2404.03179")
Connect an agent — have() is free.