1 citations · 1 across the 5 of their papers we have counts for
5 papers
Leveraging Spatiotemporal Graph Neural Networks for Multi-Store Sales Forecasting
Manish Singh, Arpita Dayama
This work evaluates the effectiveness of spatiotemporal Graph Neural Networks (GNNs) for multi-store retail sales forecasting and compares their performance against ARIMA, LSTM, an…
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer
Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng +6
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Not…
ToSA: Token Selective Attention for Efficient Vision Transformers
Manish Kumar Singh, Rajeev Yasarla, Hong Cai +2
In this paper, we propose a novel token selective attention approach, ToSA, which can identify tokens that need to be attended as well as those that can skip a transformer layer. M…
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Yunxiao Shi, Manish Kumar Singh, Hong Cai +1
In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. S…
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai +6
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimati…