24 citations · 74 across the 27 of their papers we have counts for
8 papers · 1 filter
NVIDIA Nemotron Nano V2 VL
NVIDIA, :, Amala Sanjay Deshmukh +121
We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reaso…
First Frame Is the Place to Go for Video Content Customization
Jingxi Chen, Zongxia Li, Zhichao Liu +6
What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation…
Self-Rewarding Vision-Language Model via Reasoning Decomposition
Zongxia Li, Wenhao Yu, Chengsong Huang +8
Vision-Language Models (VLMs) often suffer from visual hallucinations: generating things that are not consistent with visual inputs and language shortcuts, where they skip the visu…
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Zongxia Li, Xiyang Wu, Guangyao Shi +6
Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correla…
ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness
Yijun Liang, Ming Li, Chenrui Fan +7
Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can…
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…
AIDE: Agentically Improve Visual Language Model with Domain Experts
Ming-Chang Chiu, Fuxiao Liu, Karan Sapra +5
The enhancement of Visual Language Models (VLMs) has traditionally relied on knowledge distillation from larger, more capable models. This dependence creates a fundamental bottlene…
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
Zongxia Li, Xiyang Wu, Hongyang Du +3
Large vision-language models (VLMs) have evolved rapidly from contrastive image-text encoders and adapter-based assistants into natively multimodal foundation models that support l…