18 papers
CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering
Mingfang Zhang, Jingjing Pan, Ashutosh Kumar +7
Cause-and-effect reasoning in video is a significant challenge for Vision-Language Models (VLMs), as it requires going beyond surface-level perception to a deeper understanding of…
FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs
Eshika Khandelwal, Jingjing Pan, Mingfang Zhang +3
Multimodal large language models (MLLMs) are predominantly evaluated on free-form vision-language tasks such as visual question answering, captioning, and summarization. However, t…
Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?
Caixin Kang, Tianyu Yan, Sitong Gong +8
Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability…
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding
Ashutosh Kumar, Rajat Saini, Jingjing Pan +5
Current vision-language pre-training (VLP) paradigms excel at global scene understanding but struggle with instance-level reasoning due to global-only supervision. We introduce Ins…