activity
20242026
most citedMALT: Improving Reasoning with Multi-Agent LLM Training

3 citations · 7 across the 17 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

5 papers · 2 filters

cs.CV2024

RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation

Mingfei Han, Liang Ma, Kamila Zhumakhanova +5

Vision-and-Language Navigation (VLN) suffers from the limited diversity and scale of training data, primarily constrained by the manual curation of existing simulators. To address…

cs.CV2024★ 1 cited

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…

cs.CV2024★ 2 cited

Mitigating Object Hallucination via Concentric Causal Attention

Yun Xing, Yiheng Li, Ivan Laptev +1

Recent Large Vision Language Models (LVLMs) present remarkable zero-shot conversational and reasoning capabilities given multimodal queries. Nevertheless, they suffer from object h…

cs.CV2024

Long Story Short: Story-level Video Understanding from 20K Short Films

Ridouane Ghermi, Xi Wang, Vicky Kalogeiton +1

Recent developments in vision-language models have significantly advanced video understanding. Existing datasets and tasks, however, have notable limitations. Most datasets are con…

cs.CV2024

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models

Samar Fares, Klea Ziu, Toluwani Aremu +5

Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To…