22 citations · 79 across the 123 of their papers we have counts for
48 papers · 1 filter
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
Cong Wei, Yujie Zhong, Haoxian Tan +4
Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these me…
Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration
Haoze Sun, Wenbo Li, Jiayue Liu +7
Generalization has long been a central challenge in real-world image restoration. While recent diffusion-based restoration methods, which leverage generative priors from text-to-im…
HyperSeg: Towards Universal Visual Segmentation with Large Language Model
Cong Wei, Yujie Zhong, Haoxian Tan +4
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite signif…
LLM2: Let Large Language Models Harness System 2 Reasoning
Cheng Yang, Chufan Shi, Siheng Li +3
Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are r…
IDOL: Instant Photorealistic 3D Human Creation from a Single Image
Yiyu Zhuang, Jiaxi Lv, Hao Wen +7
Creating a high-fidelity, animatable 3D full-body avatar from a single image is a challenging task due to the diverse appearance and poses of humans and the limited availability of…
Scalable Image Tokenization with Index Backpropagation Quantization
Fengyuan Shi, Zhuoyan Luo, Yixiao Ge +3
Existing vector quantization (VQ) methods struggle with scalability, largely attributed to the instability of the codebook that undergoes partial updates during training. The codeb…
Large Language Models Can Self-Improve in Long-context Reasoning
Siheng Li, Cheng Yang, Zesen Cheng +4
Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. Existing approaches typically involve fi…
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Zicheng Lin, Tian Liang, Jiahao Xu +7
Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we int…
LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
Runming Yang, Taiqiang Wu, Jiahao Wang +4
Knowledge distillation (KD) has been a predominant method for compressing Large Language Models (LLMs). In this paper, we first revisit KD and Low-Rank Adaption (LoRA) and demonstr…
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework
Hengyuan Zhang, Chenming Shang, Sizhe Wang +6
Although fine-tuning Large Language Models (LLMs) with multilingual data can rapidly enhance the multilingual capabilities of LLMs, they still exhibit a performance gap between the…
Robust and Minimally Invasive Watermarking for EaaS
Zongqi Wang, Baoyuan Wu, Jingyuan Deng +1
Embeddings as a Service (EaaS) is emerging as a crucial role in AI applications. Unfortunately, EaaS is vulnerable to model extraction attacks, highlighting the urgent need for cop…
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Xinchen Zhang, Ling Yang, Guohao Li +6
Advanced diffusion models like RPG, Stable Diffusion 3 and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distin…
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL
Ruilin Luo, Liyuan Wang, Binghuai Lin +2
Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and…
MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts
Tianle Gu, Kexin Huang, Ruilin Luo +4
Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. LLM Unlearning, a post-hoc approach to remove this information from traine…
LoCa: Logit Calibration for Knowledge Distillation
Runming Yang, Taiqiang Wu, Yujiu Yang
Knowledge Distillation (KD), aiming to train a better student model by mimicking the teacher model, plays an important role in model compression. One typical way is to align the ou…
Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge +3
The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., codes), and achieves the state…
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Cheng Yang, Guoping Huang, Mo Yu +6
Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neura…
IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model
Yatai Ji, Shilong Zhang, Jie Wu +6
The rapid advancement of Large Vision-Language models (LVLMs) has demonstrated a spectrum of emergent capabilities. Nevertheless, current models only focus on the visual content of…
LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer
Yu Li, Yifan Chen, Gongye Liu +6
Layout generation is a foundation task of graphic design, which requires the integration of visual aesthetics and harmonious expression of content delivery. However, existing metho…
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
Jing Chen, Xinyu Zhu, Cheng Yang +8
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, la…
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
Tianle Gu, Zeyang Zhou, Kexin Huang +10
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the pra…
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
Yuxiang Zhang, Jing Chen, Junjie Wang +10
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community has yet to fully understand the…
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Cheng Yang, Chufan Shi, Yaxin Liu +11
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-i…