Search Results for author:
Found 7 papers, 5 papers with code
Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models
Vision-language models (VLMs) trained on internet-scale data achieve remarkable zero-shot detection performance on common objects like car, truck, and pedestrian.
SMORE: Simultaneous Map and Object REconstruction
We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry
Our goal is to equip health workers and public health systems with a solution for contactless newborn anthropometry in the community.
Revisiting Few-Shot Object Detection with Vision-Language Models
Concretely, we propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external data and fine-tuned on multi-modal (text and visual) K-shot examples per target class.
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach.
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions
Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus.
C3VQG: Category Consistent Cyclic Visual Question Generation
In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.
