activity
20182026
collaborators

11 papers

cs.CV2026

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

Tianjiao Yu, Xinzhuo Li, Yifan Shen +4

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

PyraTok: Language-Aligned Pyramidal Tokenizer for Video Understanding and Generation

Onkar Susladkar, Tushar Prakash, Adheesh Juvekar +4

Discrete video VAEs underpin modern text-to-video generation and video understanding systems, yet existing tokenizers typically learn visual codebooks at a single scale with limite…

cs.CV2026

Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching

Onkar Susladkar, Tushar Prakash, Gayatri Deshmukh +8

We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via task-specific l…

cs.CV2025

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…

cs.AI2025

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2018

Face Verification and Forgery Detection for Ophthalmic Surgery Images

Kaushal Bhogale, Nishant Shankar, Adheesh Juvekar +1

Although modern face verification systems are accessible and accurate, they are not always robust to pose variance and occlusions. Moreover, accurate models require a large amount…