6 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning
Yang Yue, Yulin Wang, Chenxin Tao +3
Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has eme…
HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding
Chenxin Tao, Shiqian Su, Xizhou Zhu +8
The rapid advance of Large Language Models (LLMs) has catalyzed the development of Vision-Language Models (VLMs). Monolithic VLMs, which avoid modality-specific encoders, offer a p…
Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
Weijie Su, Xizhou Zhu, Chenxin Tao +7
To effectively exploit the potential of large-scale models, various pre-training strategies supported by massive data from different sources are proposed, including supervised pre-…
BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision
Chenyu Yang, Yuntao Chen, Hao Tian +9
We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detect…
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Hao Li, Chenxin Tao, Xizhou Zhu +3
Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse…