7 papers · 1 filter
Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
Kecheng Zhang, Zongxin Yang, Mingfei Han +6
Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability that is overlooked by conventi…
LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning
Haihong Hao, Lei Chen, Mingfei Han +5
Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by action…
Saliency-R1: Incentivizing Unified Saliency Reasoning Capability in MLLM with Confidence-Guided Reinforcement Learning
Long Li, Shuichen Ji, Ziyang Luo +4
Although multimodal large language models (MLLMs) excel in high-level vision-language reasoning, they lack inherent awareness of visual saliency, making it difficult to identify ke…
Self-Consistency as a Free Lunch: Reducing Hallucinations in Vision-Language Models via Self-Reflection
Mingfei Han, Haihong Hao, Jinxing Zhou +5
Vision-language models often hallucinate details, generating non-existent objects or inaccurate attributes that compromise output reliability. Existing methods typically address th…
Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models
Longtao Jiang, Jie Huang, Mingfei Han +5
Text-guided image inpainting aims to inpaint masked image regions based on a textual prompt while preserving the background. Although diffusion-based methods have become dominant,…
Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation
Jinxing Zhou, Zhihui Li, Yongqiang Yu +7
We present \textbf{Met}a-\textbf{T}oken \textbf{Le}arning (Mettle), a simple and memory-efficient method for adapting large-scale pretrained transformer models to downstream audio-…
Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation
Sihao Lin, Daqi Liu, Ruochong Fu +6
Estimating the 3D world from 2D monocular images is a fundamental yet challenging task due to the labour-intensive nature of 3D annotations. To simplify label acquisition, this wor…