31 citations · 52 across the 7 of their papers we have counts for
7 papers
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
Neeraj Anand, Samyak Jha, Udbhav Bamba +1
Despite the rapid success of Large Vision-Language Models (LVLMs), a persistent challenge is their tendency to generate hallucinated content, undermining reliability in real-world…
C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation,…
A Generalized & Robust Framework For Timestamp Supervision in Temporal Action Segmentation
Rahul Rahaman, Dipika Singhania, Alexandre Thiery +1
In temporal action segmentation, Timestamp supervision requires only a handful of labelled frames per video sequence. For unlabelled frames, previous works rely on assigning hard l…
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method…
Coarse to Fine Multi-Resolution Temporal Convolutional Network
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal convolutional networks (TCNs) are a commonly used architecture for temporal video segmentation. TCNs however, tend to suffer from over-segmentation errors and require addi…
Pretrained equivariant features improve unsupervised landmark discovery
Rahul Rahaman, Atin Ghosh, Alexandre H. Thiery
Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with groun…
Uncertainty Quantification and Deep Ensembles
Rahul Rahaman, Alexandre H. Thiery
Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although…