{"ID":2844294,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06261","arxiv_id":"2511.06261","title":"Robust Nearest Neighbour Retrieval Using Targeted Manifold Manipulation","abstract":"Nearest-neighbour retrieval is central to classification and explainable-AI pipelines, but current practice relies on hand-tuning feature layers and distance metrics. We propose Targeted Manifold Manipulation-Nearest Neighbour (TMM-NN), which reconceptualises retrieval by assessing how readily each sample can be nudged into a designated region of the feature manifold; neighbourhoods are defined by a sample's responsiveness to a targeted perturbation rather than absolute geometric distance. TMM-NN implements this through a lightweight, query-specific trigger patch. The patch is added to the query image, and the network is weakly ``backdoored'' so that any input with the patch is steered toward a dummy class. Images similar to the query need only a slight shift and are classified as the dummy class with high probability, while dissimilar ones are less affected. By ranking candidates by this confidence, TMM-NN retrieves the most semantically related neighbours. Robustness analysis and benchmark experiments confirm this trigger-based ranking outperforms traditional metrics under noise and across diverse tasks.","short_abstract":"Nearest-neighbour retrieval is central to classification and explainable-AI pipelines, but current practice relies on hand-tuning feature layers and distance metrics. We propose Targeted Manifold Manipulation-Nearest Neighbour (TMM-NN), which reconceptualises retrieval by assessing how readily each sample can be nudged...","url_abs":"https://arxiv.org/abs/2511.06261","url_pdf":"https://arxiv.org/pdf/2511.06261v2","authors":"[\"B. Ghosh\",\"H. Harikumar\",\"S. Rana\"]","published":"2025-11-09T07:37:05Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}