We lifted 4 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| nkusyl/fvit | canonical | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| build_transform | Ran | nkusyl/fvit/datasets.py code served (permissive licence) · get_code("ec05b468041ed6af") |
| build_backbone | Not yet run | nkusyl/fvit/model/FViT.py code served (permissive licence) · get_code("3b6be43bd23e2f65") |
| build_dataset | Not yet run | nkusyl/fvit/datasets.py code served (permissive licence) · get_code("ed28cb59239cd994") |
| sfc_flop_jit | Not yet run | nkusyl/fvit/utils.py code served (permissive licence) · get_code("2a2617c52f0e6b56") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Vision transformers have achieved encouraging progress in various computer vision tasks. A common belief is that this is attributed to the capability of self-attention in modeling the global dependencies among feature tokens. However, self-attention still faces several challenges in dense prediction tasks, including high computational complexity and absence of desirable inductive bias. To alleviate these issues, the potential advantages of combining vision transformers with Gabor filters are revisited, and a learnable Gabor filter (LGF) using convolution is proposed. The LGF does not rely on self-attention, and it is used to simulate the response of fundamental cells in the biological visual system to the input images. This encourages vision transformers to focus on discriminative feature representations of targets across different scales and orientations. In addition, a Bionic Focal Vision (BFV) block is designed based on the LGF. This block draws inspiration from neuroscience and introduces a Dual-Path Feed Forward Network (DPFFN) to emulate the parallel and cascaded information processing scheme of the biological visual cortex. Furthermore, a unified and efficient family of pyramid backbone networks called Focal Vision Transformers (FViTs) is developed by stacking BFV blocks. Experimental results indicate that FViTs demonstrate superior performance in various vision tasks. In terms of computational efficiency and scalability, FViTs show significant advantages compared with other counterparts.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.11303")
get_code_for_paper("2402.11303")
have("2402.11303")
Connect an agent — have() is free.