283 citations · 778 across the 23 of their papers we have counts for
18 papers · 1 filter
Panoptic Video Scene Graph Generation
Jingkang Yang, Wenxuan Peng, Xiangtai Li +8
Towards building comprehensive real-world visual perception systems, we propose and study a new problem called panoptic scene graph generation (PVSG). PVSG relates to the existing…
OtterHD: A High-Resolution Multi-modality Model
Bo Li, Peiyuan Zhang, Jingkang Yang +3
In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precisio…
Large Language Models are Visual Reasoning Coordinators
Liangyu Chen, Bo Li, Sheng Shen +5
Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsen…
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Jingkang Yang, Yuhao Dong, Shuai Liu +8
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works e…
Link-Context Learning for Multimodal LLMs
Yan Tai, Weichen Fan, Zhao Zhang +3
The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLL…
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
Lingting Zhu, Zeyue Xue, Zhenchao Jin +4
Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-…
Benchmarking and Analyzing Generative Data for Visual Recognition
Bo Li, Haotian Liu, Liangyu Chen +3
Advancements in large pre-trained generative models have expanded their potential as effective data generators in visual recognition. This work delves into the impact of generative…
Pair then Relation: Pair-Net for Panoptic Scene Graph Generation
Jinghao Wang, Zhengyu Wen, Xiangtai Li +3
Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation ins…
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen +5
High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language…
FunQA: Towards Surprising Video Comprehension
Binzhu Xie, Sicheng Zhang, Zitang Zhou +5
Surprising videos, such as funny clips, creative performances, or visual illusions, attract significant attention. Enjoyment of these videos is not simply a response to visual stim…
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Jingyang Zhang, Jingkang Yang, Pengyun Wang +9
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods…
Learning without Forgetting for Vision-Language Models
Da-Wei Zhou, Yuanhan Zhang, Yan Wang +4
Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former o…
Otter: A Multi-Modal Model with In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen +5
Recent advances in Large Multimodal Models (LMMs) have unveiled great potential as visual assistants. However, most existing works focus on responding to individual instructions or…
Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation
Lingting Zhu, Xian Liu, Xuanyu Liu +3
Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial netwo…
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye +2
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
Class-Incremental Learning: A Survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi +3
Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in…
What Makes Good Examples for Visual In-Context Learning?
Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu
Large-scale models trained on broad data have recently become the mainstream architecture in computer vision due to their strong generalization performance. In this paper, the main…