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20232025
most citedAre We on the Right Way for Evaluating Large Vision-Language Models?

9 citations · 37 across the 31 of their papers we have counts for

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12 papers · 1 filter

cs.CV2025

ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing

Long Xing, Qidong Huang, Xiaoyi Dong +10

This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality imag…

cs.IR2025

Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings

Yubo Ma, Jinsong Li, Yuhang Zang +8

Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), it encodes each page into multiple patch-level embeddings and leads to excessive memory u…

cs.CV2025

HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance

Jiazi Bu, Pengyang Ling, Yujie Zhou +7

Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution im…

cs.CV2025

MM-IFEngine: Towards Multimodal Instruction Following

Shengyuan Ding, Shenxi Wu, Xiangyu Zhao +7

The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and whether they are doing it righ…

cs.CV2025

Light-A-Video: Training-free Video Relighting via Progressive Light Fusion

Yujie Zhou, Jiazi Bu, Pengyang Ling +10

Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video…

cs.CL2025

BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning

Beichen Zhang, Yuhong Liu, Xiaoyi Dong +6

Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed…

cs.SD2025★ 1 cited

SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation

Zihan Liu, Shuangrui Ding, Zhixiong Zhang +6

Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approa…

cs.CV2025

VideoRoPE: What Makes for Good Video Rotary Position Embedding?

Xilin Wei, Xiaoran Liu, Yuhang Zang +9

While Rotary Position Embedding (RoPE) and its variants are widely adopted for their long-context capabilities, the extension of the 1D RoPE to video, with its complex spatio-tempo…

cs.CV2025

RelightVid: Temporal-Consistent Diffusion Model for Video Relighting

Ye Fang, Zeyi Sun, Shangzhan Zhang +6

Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these…

cs.CV2025

Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction

Rui Qian, Shuangrui Ding, Xiaoyi Dong +5

Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while cont…

cs.CV2025

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model

Yuhang Zang, Xiaoyi Dong, Pan Zhang +10

Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs. While reward models (RMs) with rein…

cs.CV2025

OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?

Yifei Li, Junbo Niu, Ziyang Miao +12

Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline…