77 citations · 304 across the 92 of their papers we have counts for
9 papers · 1 filter
HG-Bench: A Benchmark for Multi-Page Handwritten Answer-Region Grounding in Automated Homework Assessment
Chuangxin Zhao, Boyan Shi, Yanling Wang +7
Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate reasoning step appears in noi…
MMGeoLM: Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Models
Kai Sun, Yushi Bai, Zhen Yang +4
Large Multimodal Models (LMMs) typically build on ViTs (e.g., CLIP), yet their training with simple random in-batch negatives limits the ability to capture fine-grained visual diff…
LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
Shangqing Tu, Yucheng Wang, Daniel Zhang-Li +8
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000…
CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning
Ji Qi, Ming Ding, Weihan Wang +8
Vision-Language Models (VLMs) have demonstrated their broad effectiveness thanks to extensive training in aligning visual instructions to responses. However, such training of concl…
CogVLM: Visual Expert for Pretrained Language Models
Weihan Wang, Qingsong Lv, Wenmeng Yu +13
We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of…
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools
Ji Qi, Kaixuan Ji, Jifan Yu +4
Building models that comprehends videos and responds specific user instructions is a practical and challenging topic, as it requires mastery of both vision understanding and knowle…
TBench: Benchmarking Current Progress in Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +6
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3…
ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation
Weihan Wang, Zhen Yang, Bin Xu +2
Vision-language pre-training (VLP) methods are blossoming recently, and its crucial goal is to jointly learn visual and textual features via a transformer-based architecture, demon…
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Ji Qi, Jifan Yu, Teng Tu +12
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commen…