Topological RANSAC for instance verification and retrieval without fine-tuning

This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance recognition, leading to inherent limitations, including the assumption of planar structures and neglect of topological relations among features. To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process. We propose bio-inspired saccade and fovea functions to verify the topological consistency among features, effectively circumventing the issues associated with SP's spatial model. Our experimental results demonstrate that our method significantly outperforms SP, achieving state-of-the-art performance in non-fine-tuning retrieval. Furthermore, our approach can enhance performance when used in conjunction with fine-tuned features. Importantly, our method retains high explainability and is lightweight, offering a practical and adaptable solution for a variety of real-world applications

Towards Content-based Pixel Retrieval in Revisited Oxford and Paris

This paper introduces the first two pixel retrieval benchmarks. Pixel retrieval is segmented instance retrieval. Like semantic segmentation extends classification to the pixel level, pixel retrieval is an extension of image retrieval and offers information about which pixels are related to the query object. In addition to retrieving images for the given query, it helps users quickly identify the query object in true positive images and exclude false positive images by denoting the correlated pixels. Our user study results show pixel-level annotation can significantly improve the user experience. Compared with semantic and instance segmentation, pixel retrieval requires a fine-grained recognition capability for variable-granularity targets. To this end, we propose pixel retrieval benchmarks named PROxford and PRParis, which are based on the widely used image retrieval datasets, ROxford and RParis. Three professional annotators label 5,942 images with two rounds of double-checking and refinement. Furthermore, we conduct extensive experiments and analysis on the SOTA methods in image search, image matching, detection, segmentation, and dense matching using our pixel retrieval benchmarks. Results show that the pixel retrieval task is challenging to these approaches and distinctive from existing problems, suggesting that further research can advance the content-based pixel-retrieval and thus user search experience. The datasets can be downloaded from \href{https://github.com/anguoyuan/Pixel_retrieval-Segmented_instance_retrieval}{this link}

MLKL deficiency alleviates neuroinflammation and motor deficits in the α-synuclein transgenic mouse model of Parkinson’s disease

Abstract Parkinson’s disease (PD), one of the most devastating neurodegenerative brain disorders, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra (SN) and deposits of α-synuclein aggregates. Currently, pharmacological interventions for PD remain inadequate. The cell necroptosis executor protein MLKL (Mixed-lineage kinase domain-like) is involved in various diseases, including inflammatory bowel disease and neurodegenerative diseases; however, its precise role in PD remains unclear. Here, we investigated the neuroprotective role of MLKL inhibition or ablation against primary neuronal cells and human iPSC-derived midbrain organoids induced by toxic α-Synuclein preformed fibrils (PFFs). Using a mouse model (Tg-Mlkl −/− ) generated by crossbreeding the SNCA A53T synuclein transgenic mice with MLKL knockout (KO)mice, we assessed the impact of MLKL deficiency on the progression of Parkinsonian traits. Our findings demonstrate that Tg-Mlkl −/− mice exhibited a significant improvement in motor symptoms and reduced phosphorylated α-synuclein expression compared to the classic A53T transgenic mice. Furthermore, MLKL deficiency alleviated tyrosine hydroxylase (TH)-positive neuron loss and attenuated neuroinflammation by inhibiting the activation of microglia and astrocytes. Single-cell RNA-seq (scRNA-seq) analysis of the SN of Tg-Mlkl −/− mice revealed a unique cell type-specific transcriptome profile, including downregulated prostaglandin D synthase (PTGDS) expression, indicating reduced microglial cells and dampened neuron death. Thus, MLKL represents a critical therapeutic target for reducing neuroinflammation and preventing motor deficits in PD