4 citations · 13 across the 30 of their papers we have counts for
16 papers · 1 filter
Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes
Jing Tan, Zhaoyang Zhang, Yantao Shen +6
We introduce Talk2Move, a reinforcement learning (RL) based diffusion framework for text-instructed spatial transformation of objects within scenes. Spatially manipulating objects…
Descriminative-Generative Custom Tokens for Vision-Language Models
Pramuditha Perera, Matthew Trager, Luca Zancato +2
This paper explores the possibility of learning custom tokens for representing new concepts in Vision-Language Models (VLMs). Our aim is to learn tokens that can be effective for b…
NeRF-Insert: 3D Local Editing with Multimodal Control Signals
Benet Oriol Sabat, Alessandro Achille, Matthew Trager +1
We propose NeRF-Insert, a NeRF editing framework that allows users to make high-quality local edits with a flexible level of control. Unlike previous work that relied on image-to-i…
Multi-Modal Hallucination Control by Visual Information Grounding
Alessandro Favero, Luca Zancato, Matthew Trager +5
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phe…
Enhancing Vision-Language Pre-training with Rich Supervisions
Yuan Gao, Kunyu Shi, Pengkai Zhu +7
We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. U…
Non-autoregressive Sequence-to-Sequence Vision-Language Models
Kunyu Shi, Qi Dong, Luis Goncalves +2
Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predict…