1 citations · 2 across the 7 of their papers we have counts for
9 papers
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…
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…
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…
Out-of-Distribution Detection with Overlap Index
Hao Fu, Prashanth Krishnamurthy, Siddharth Garg +1
Out-of-distribution (OOD) detection is crucial for the deployment of machine learning models in the open world. While existing OOD detectors are effective in identifying OOD sample…
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…
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
Georg Pichler, Marco Romanelli, Divya Prakash Manivannan +3
We introduce a formal statistical definition for the problem of backdoor detection in machine learning systems and use it to analyze the feasibility of such problems, providing evi…
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Patricia Pauli, Aaron Havens, Alexandre Araujo +4
Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab…
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…