activity
20182026
most citedCascadedGaze: Efficiency in Global Context Extraction for Image Restoration

9 citations · 9 across the 6 of their papers we have counts for

collaborators

13 papers

cs.CV2026

Don't Show Pixels, Show Cues: Unlocking Visual Tool Reasoning in Language Models via Perception Programs

Muhammad Kamran Janjua, Hugo Silva, Di Niu +1

Multimodal language models (MLLMs) are increasingly paired with vision tools (e.g., depth, flow, correspondence) to enhance visual reasoning. However, despite access to these tool-…

cs.CV2026

Panoptic Pairwise Distortion Graph

Muhammad Kamran Janjua, Abdul Wahab, Bahador Rashidi

In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods…

cs.CV2025

Grounding Degradations in Natural Language for All-In-One Video Restoration

Muhammad Kamran Janjua, Amirhosein Ghasemabadi, Kunlin Zhang +3

In this work, we propose an all-in-one video restoration framework that grounds degradation-aware semantic context of video frames in natural language via foundation models, offeri…

cs.LG2025

Fantastic Multi-Task Gradient Updates and How to Find Them In a Cone

Negar Hassanpour, Muhammad Kamran Janjua, Kunlin Zhang +4

Balancing competing objectives remains a fundamental challenge in multi-task learning (MTL), primarily due to conflicting gradients across individual tasks. A common solution relie…

cs.CV2024

Learning Truncated Causal History Model for Video Restoration

Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh +1

One key challenge to video restoration is to model the transition dynamics of video frames governed by motion. In this work, we propose TURTLE to learn the truncated causal history…

cs.CV2020

Movement-induced Priors for Deep Stereo

Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala +1

We propose a method for fusing stereo disparity estimation with movement-induced prior information. Instead of independent inference frame-by-frame, we formulate the problem as a n…