6 papers
Seeing Roads Through Words: A Language-Guided Framework for RGB-T Driving Scene Segmentation
Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo +2
Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications. RGB-Thermal fus…
KnowDiffuser: A Knowledge-Guided Diffusion Planner with LLM Reasoning
Fan Ding, Xuewen Luo, Fengze Yang +4
Recent advancements in Language Models (LMs) have demonstrated strong semantic reasoning capabilities, enabling their application in high-level decision-making for autonomous drivi…
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo +2
Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployme…
A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Lemuel Lai En Che +3
Reconstruction of High Dynamic Range (HDR) from Low Dynamic Range (LDR) images is an important computer vision task. There is a significant amount of research utilizing both conven…
Seeing in the Dark: A Teacher-Student Framework for Dark Video Action Recognition via Knowledge Distillation and Contrastive Learning
Sharana Dharshikgan Suresh Dass, Hrishav Bakul Barua, Ganesh Krishnasamy +2
Action recognition in dark or low-light (under-exposed) videos is a challenging task due to visibility degradation, which can hinder critical spatiotemporal details. This paper pro…
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Ganesh Krishnasamy +2
Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed t…