VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition (ECCV 2024)

Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).

This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

Contains our Approach for the competition organized at Udyam'21

Acoustic Communication Underwater Mimicking Sea Classification by Multiscale Deep Features Aggregation and Low Complexity, and Data Augmentation.

This project enhances text encoders' global descriptors by implementing advanced aggregation techniques from computer vision literature, to improve sentence-level representations. We utilize models such as BERT, RoBERTa, and CLIP, and benchmark performance using datasets like MTEB, SemEval24 and Quora Question Pairs.