Learning What Helps: Task-Aligned Context Selection for Vision Tasks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908681741795328 |
|---|---|
| author | Guo, Jingyu Konuk, Emir Strand, Fredrik Matsoukas, Christos Smith, Kevin |
| author_facet | Guo, Jingyu Konuk, Emir Strand, Fredrik Matsoukas, Christos Smith, Kevin |
| contents | Humans often resolve visual uncertainty by comparing an image with relevant examples, but ViTs lack the ability to identify which examples would improve their predictions. We present Task-Aligned Context Selection (TACS), a framework that learns to select paired examples which truly improve task performance rather than those that merely appear similar. TACS jointly trains a selector network with the task model through a hybrid optimization scheme combining gradient-based supervision and reinforcement learning, making retrieval part of the learning objective. By aligning selection with task rewards, TACS enables discriminative models to discover which contextual examples genuinely help. Across 18 datasets covering fine-grained recognition, medical image classification, and medical image segmentation, TACS consistently outperforms similarity-based retrieval, particularly in challenging or data-limited settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning What Helps: Task-Aligned Context Selection for Vision Tasks Guo, Jingyu Konuk, Emir Strand, Fredrik Matsoukas, Christos Smith, Kevin Computer Vision and Pattern Recognition Humans often resolve visual uncertainty by comparing an image with relevant examples, but ViTs lack the ability to identify which examples would improve their predictions. We present Task-Aligned Context Selection (TACS), a framework that learns to select paired examples which truly improve task performance rather than those that merely appear similar. TACS jointly trains a selector network with the task model through a hybrid optimization scheme combining gradient-based supervision and reinforcement learning, making retrieval part of the learning objective. By aligning selection with task rewards, TACS enables discriminative models to discover which contextual examples genuinely help. Across 18 datasets covering fine-grained recognition, medical image classification, and medical image segmentation, TACS consistently outperforms similarity-based retrieval, particularly in challenging or data-limited settings. |
| title | Learning What Helps: Task-Aligned Context Selection for Vision Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.00489 |