2 citations · 4 across the 8 of their papers we have counts for
8 papers
SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective
Yuyao Wang, Min Yang, Meng Chen +3
Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities adopt incompatible partitions an…
FlowGuard: Towards Lightweight In-Generation Safety Detection for Diffusion Models via Linear Latent Decoding
Jinghan Yang, Yihe Fan, Xudong Pan +1
Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) content. Existing NSFW detection me…
SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
Yirong Sun, Yanjun Chen, Xin Qiu +8
Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness,…
Visual Merit or Linguistic Crutch? A Close Look at DeepSeek-OCR
Yunhao Liang, Ruixuan Ying, Bo Li +7
DeepSeek-OCR utilizes an optical 2D mapping approach to achieve high-ratio vision-text compression, claiming to decode text tokens exceeding ten times the input visual tokens. Whil…
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Chenhao Zhang, Xi Feng, Yuelin Bai +18
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Ziqiang Liu, Feiteng Fang, Xi Feng +23
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
Feiteng Fang, Yuelin Bai, Shiwen Ni +3
Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-…
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
Xiaojun Chen, Tianle Wang, Tianhao Qiu +2
Despite the success of large language models (LLMs) in Text-to-SQL tasks, open-source LLMs encounter challenges in contextual understanding and response coherence. To tackle these…