47 citations · 172 across the 92 of their papers we have counts for
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SYNCR: Diagnosing and Learning Cross-Video Reasoning from Simulation
Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy +1
Reasoning across videos requires aligning events, matching identities, comparing motion, and integrating partial observations. Evaluating these capabilities and testing how to impr…
SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding
Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy +1
Multimodal Large Language Models (MLLMs) have made rapid progress in single-video understanding, yet their ability to reason across multiple independent video streams remains poorl…
BOP-ASK: Object-Interaction Reasoning for Vision-Language Models
Vineet Bhat, Sungsu Kim, Valts Blukis +6
Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interacti…
Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning
Sara Ghazanfari, Francesco Croce, Nicolas Flammarion +3
Recent work has shown that eliciting Large Language Models (LLMs) to generate reasoning traces in natural language before answering the user's request can significantly improve the…
RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation
Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami
Mapping and understanding complex 3D environments is fundamental to how autonomous systems perceive and interact with the physical world, requiring both precise geometric reconstru…
Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics
Sara Ghazanfari, Siddharth Garg, Nicolas Flammarion +3
Human perception of similarity across uni- and multimodal inputs is highly complex, making it challenging to develop automated metrics that accurately mimic it. General purpose vis…
EMMA: Efficient Visual Alignment in Multi-Modal LLMs
Sara Ghazanfari, Alexandre Araujo, Prashanth Krishnamurthy +2
Multi-modal Large Language Models (MLLMs) have recently exhibited impressive general-purpose capabilities by leveraging vision foundation models to encode the core concepts of imag…
FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training
Raktim Gautam Goswami, Naman Patel, Prashanth Krishnamurthy +1
Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need…
SALSA: Swift Adaptive Lightweight Self-Attention for Enhanced LiDAR Place Recognition
Raktim Gautam Goswami, Naman Patel, Prashanth Krishnamurthy +1
Large-scale LiDAR mappings and localization leverage place recognition techniques to mitigate odometry drifts, ensuring accurate mapping. These techniques utilize scene representat…
CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring
Hao Fu, Naman Patel, Prashanth Krishnamurthy +1
Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models. Recent advances in vision language foundation models have made…
LipSim: A Provably Robust Perceptual Similarity Metric
Sara Ghazanfari, Alexandre Araujo, Prashanth Krishnamurthy +2
Recent years have seen growing interest in developing and applying perceptual similarity metrics. Research has shown the superiority of perceptual metrics over pixel-wise metrics i…
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy +2
Similarity metrics have played a significant role in computer vision to capture the underlying semantics of images. In recent years, advanced similarity metrics, such as the Learne…
Towards Better Certified Segmentation via Diffusion Models
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +4
The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classif…