activity
20172026
most citedImproving Distantly Supervised Relation Extraction using Word and Entity Based Attention

103 citations · 233 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Modular Diffusion Models for Structured Visual Recognition

Siddhesh Khandelwal, Björn Ommer, Leonid Sigal

Traditional supervised methods for structured visual recognition tasks -- such as object detection, segmentation, and scene graph generation -- often produce deterministic, fixed o…

cs.CV2023

Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models

Jiayun Luo, Siddhesh Khandelwal, Leonid Sigal +1

From image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which prove effective for tasks like visual question answer…

cs.CV2023★ 4 cited

Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline

Siddhesh Khandelwal, Anirudth Nambirajan, Behjat Siddiquie +2

Methods for object detection and segmentation often require abundant instance-level annotations for training, which are time-consuming and expensive to collect. To address this, th…

cs.CV2023★ 92 cited

Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

Benjamin Wilson, William Qi, Tanmay Agarwal +10

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequ…

cs.CV2022★ 11 cited

Iterative Scene Graph Generation

Siddhesh Khandelwal, Leonid Sigal

The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large…

cs.CV2021

Segmentation-grounded Scene Graph Generation

Siddhesh Khandelwal, Mohammed Suhail, Leonid Sigal

Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an…

cs.LG2020★ 23 cited

What-If Motion Prediction for Autonomous Driving

Siddhesh Khandelwal, William Qi, Jagjeet Singh +2

Forecasting the long-term future motion of road actors is a core challenge to the deployment of safe autonomous vehicles (AVs). Viable solutions must account for both the static ge…

cs.CV2020

UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

Siddhesh Khandelwal, Raghav Goyal, Leonid Sigal

Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate t…

cs.CV2019

AttentionRNN: A Structured Spatial Attention Mechanism

Siddhesh Khandelwal, Leonid Sigal

Visual attention mechanisms have proven to be integrally important constituent components of many modern deep neural architectures. They provide an efficient and effective way to u…

cs.CL2018★ 103 cited

Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

Sharmistha Jat, Siddhesh Khandelwal, Partha Talukdar

Relation extraction is the problem of classifying the relationship between two entities in a given sentence. Distant Supervision (DS) is a popular technique for developing relation…

cs.LG2017

Faster K-Means Cluster Estimation

Siddhesh Khandelwal, Amit Awekar

There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants ten…