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20242026
most citedDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

884 citations · 885 across the 17 of their papers we have counts for

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cs.CV2026

MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning

Shuo Xu, Sai Wang, Xinyue Hu +3

Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives (attributes and objects) from seen compositions and recognize unseen attribute-object compositions. Existi…

cs.CV2026

FROST-Drive: Scalable and Efficient End-to-End Driving with a Frozen Vision Encoder

Zeyu Dong, Yimin Zhu, Yu Wu +1

End-to-end (E2E) models in autonomous driving aim to directly map sensor inputs to control commands, but their ability to generalize to novel and complex scenarios remains a key ch…

cs.CV2025

TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction

Sai Wang, Fan Ma, Xinyi Li +2

Recent advancements in LLMs have accelerated the development of dialogue generation across text and images, yet video-based dialogue generation remains underexplored and presents u…

cs.CV2024

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Zhiyu Wu, Xiaokang Chen, Zizheng Pan +24

We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key…

cs.CV2024

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Long Qian, Juncheng Li, Yu Wu +5

Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video mo…