1 citations · 2 across the 10 of their papers we have counts for
11 papers
StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing
Han Wang, Deyi Ji, Lanyun Zhu +2
Live streaming platforms require real-time monitoring and reaction to social signals, utilizing partial and asynchronous evidence from video, text, and audio. We propose StreamSens…
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
MIRA: Multimodal Iterative Reasoning Agent for Image Editing
Ziyun Zeng, Hang Hua, Jiebo Luo
Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interp…
Latent Chain-of-Thought for Visual Reasoning
Guohao Sun, Hang Hua, Jian Wang +5
Chain-of-thought (CoT) reasoning is critical for improving the interpretability and reliability of Large Vision-Language Models (LVLMs). However, existing training algorithms such…
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Yunlong Tang, Jing Bi, Chao Huang +16
We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects…
SafeCFG: Controlling Harmful Features with Dynamic Safe Guidance for Safe Generation
Jiadong Pan, Liang Li, Hongcheng Gao +3
Diffusion models (DMs) have demonstrated exceptional performance in text-to-image tasks, leading to their widespread use. With the introduction of classifier-free guidance (CFG), t…
FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity
Hang Hua, Qing Liu, Lingzhi Zhang +5
The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal tasks, enabling more sophisticated and accurate reasoning across various applications, inclu…
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Cheng Han, James Chenhao Liang +6
Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic i…
Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding
Yue Xu, Kaizhi Yang, Jiebo Luo +1
3D visual grounding is an emerging research area dedicated to making connections between the 3D physical world and natural language, which is crucial for achieving embodied intelli…
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
Yuying Li, Zeyan Liu, Junyi Zhao +4
Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photog…
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
Hang Hua, Jing Shi, Kushal Kafle +5
Recent progress in large-scale pre-training has led to the development of advanced vision-language models (VLMs) with remarkable proficiency in comprehending and generating multimo…