924 citations · 1.5k across the 16 of their papers we have counts for
7 papers · 1 filter
StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring
Xiangyue Zhang, Jianfang Li, Jiaxu Zhang +2
Real-time co-speech gesture generation must produce 3D motion clip by clip as speech arrives. Existing streaming methods are open-loop: each clip depends on past context, but the m…
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing
Dongxu Li, Junnan Li, Steven C. H. Hoi
Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties p…
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Wenliang Dai, Junnan Li, Dongxu Li +6
Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-lan…
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Junnan Li, Dongxu Li, Silvio Savarese +1
The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficie…
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
Jiaxian Guo, Junnan Li, Dongxu Li +4
Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answer…
Masked Unsupervised Self-training for Label-free Image Classification
Junnan Li, Silvio Savarese, Steven C. H. Hoi
State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost.…
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
Gao Peng, Zhengkai Jiang, Haoxuan You +4
Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra-…