110 citations · 120 across the 15 of their papers we have counts for
17 papers
One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing
Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen +6
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We in…
GraphVid: Interactive Graph-Controllable Video Generation
Vedant Shah, Onkar Susladkar, Tushar Prakash +5
Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily c…
Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
Xiaona Zhou, Muntasir Wahed, Tianjiao Yu +2
Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large lan…
RewardFlow: Generate Images by Optimizing What You Reward
Onkar Susladkar, Dong-Hwan Jang, Tushar Prakash +7
We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward Langevin dynamics. RewardFlow…
DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Tianjiao Yu, Xinzhuo Li, Muntasir Wahed +4
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic…
BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
Fahim Ahmed, Md Mubtasim Ahasan, Jahir Sadik Monon +4
Text-to-SQL systems provide a natural language interface that can enable even laymen to access information stored in databases. However, existing Large Language Models (LLM) strugg…
MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?
Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen +5
Recent advancements in Large Language Models (LLMs) have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particular…
PurpCode: Reasoning for Safer Code Generation
Jiawei Liu, Nirav Diwan, Zhe Wang +11
We introduce PurpCode, the first post-training recipe for training safe code reasoning models towards generating secure code and defending against malicious cyberactivities. PurpCo…
Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination
Xinzhuo Li, Adheesh Juvekar, Jiaxun Zhang +6
Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding hallucinations, producing masks for i…
PartGS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
Tianjiao Yu, Vedant Shah, Muntasir Wahed +3
Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$…
Uncertainty in Action: Confidence Elicitation in Embodied Agents
Tianjiao Yu, Vedant Shah, Muntasir Wahed +4
Expressing confidence is challenging for embodied agents navigating dynamic multimodal environments, where uncertainty arises from both perception and decision-making processes. We…
CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models
Kiet A. Nguyen, Adheesh Juvekar, Tianjiao Yu +2
Recent advances in Large Vision-Language Models (LVLMs) have enabled general-purpose vision tasks through visual instruction tuning. While existing LVLMs can generate segmentation…
PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation
Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar +6
Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to…
Hard Negative Sampling Strategies for Contrastive Representation Learning
Afrina Tabassum, Muntasir Wahed, Hoda Eldardiry +1
One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information. Random sampling or importance sa…
Adversarial Contrastive Learning by Permuting Cluster Assignments
Muntasir Wahed, Afrina Tabassum, Ismini Lourentzou
Contrastive learning has gained popularity as an effective self-supervised representation learning technique. Several research directions improve traditional contrastive approaches…
SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion
Muntasir Wahed, Daniel Gruhl, Alfredo Alba +5
Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined n…
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
Nur Ahmed, Muntasir Wahed
Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, o…