2 citations · 3 across the 15 of their papers we have counts for
16 papers
Visual Attention Faithfulness in Vision-Language Models is Heterogeneous
Xurui Song, Weishi Wang, Zhongqi Yue +5
Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Langua…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation
Bo Miao, Weijia Liu, Jun Luo +8
Language-conditioned goal navigation (LGN) requires embodied agents to locate user-specified targets without step-by-step guidance. However, existing benchmarks largely focus on ca…
SmoothCLAP: Soft-Target Enhanced Contrastive Language\--Audio Pretraining for Affective Computing
Xin Jing, Jiadong Wang, Andreas Triantafyllopoulos +4
The ambiguity of human emotions poses several challenges for machine learning models, as they often overlap and lack clear delineating boundaries. Contrastive language-audio pretra…
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
Xurui Song, Shuo Huai, JingJing Jiang +2
Vision-Language Model (VLM) driving agents promise explainable end-to-end autonomy by first producing natural-language reasoning and then predicting trajectory planning. However, w…
Rehearsal-free and Task-free Online Continual Learning With Contrastive Prompt
Aopeng Wang, Ke Deng, Yongli Ren +1
The main challenge of continual learning is \textit{catastrophic forgetting}. Because of processing data in one pass, online continual learning (OCL) is one of the most difficult c…
Reinitializing weights vs units for maintaining plasticity in neural networks
J. Fernando Hernandez-Garcia, Shibhansh Dohare, Jun Luo +1
Loss of plasticity is a phenomenon in which a neural network loses its ability to learn when trained for an extended time on non-stationary data. It is a crucial problem to overcom…
Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoning
Jingjing Jiang, Chao Ma, Xurui Song +2
Recent advancements in multimodal large language models (MLLMs) have demonstrated exceptional performance in multimodal perception and understanding. However, leading open-source M…
A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations
Tian Lan, Yang-Hao Zhou, Zi-Ao Ma +8
Recent advances in deep learning have significantly enhanced generative AI capabilities across text, images, and audio. However, automatically evaluating the quality of these gener…
Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Ziwei Liu, Borui Kang, Wei Li +6
Vision-Language Continual Learning (VLCL) has attracted significant research attention for its robust capabilities, and the adoption of Parameter-Efficient Fine-Tuning (PEFT) strat…
Can Large Language Models Understand Internet Buzzwords Through User-Generated Content
Chen Huang, Junkai Luo, Xinzuo Wang +2
The massive user-generated content (UGC) available in Chinese social media is giving rise to the possibility of studying internet buzzwords. In this paper, we study if large langua…
Co-Reinforcement Learning for Unified Multimodal Understanding and Generation
Jingjing Jiang, Chongjie Si, Jun Luo +2
This paper presents a pioneering exploration of reinforcement learning (RL) via group relative policy optimization for unified multimodal large language models (ULMs), aimed at sim…
An Evaluation Framework for Product Images Background Inpainting based on Human Feedback and Product Consistency
Yuqi Liang, Jun Luo, Xiaoxi Guo +1
In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques st…
On the Performance Analysis of Momentum Method: A Frequency Domain Perspective
Xianliang Li, Jun Luo, Zhiwei Zheng +5
Momentum-based optimizers are widely adopted for training neural networks. However, the optimal selection of momentum coefficients remains elusive. This uncertainty impedes a clear…
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
Indraneil Paul, Goran Glavaš, Iryna Gurevych
Code understanding and generation have fast become some of the most popular applications of language models (LMs). Nonetheless, research on multilingual aspects of Code-LMs (i.e.,…
Ask Language Model to Clean Your Noisy Translation Data
Quinten Bolding, Baohao Liao, Brandon James Denis +2
Transformer models have demonstrated remarkable performance in neural machine translation (NMT). However, their vulnerability to noisy input poses a significant challenge in practi…