Search Results for author:
Found 8 papers, 6 papers with code
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX.
Ranked #4 on
Video Question Answering
on MVBench
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training
Firstly, we observe that the CLIP's visual feature of image subregions can achieve closer proximity to the paired caption due to the inherent information loss in text descriptions.
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks.
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings.
Ranked #2 on
Visual Question Answering
on BenchLMM
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models
In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection.
Ranked #8 on
Human-Object Interaction Detection
on V-COCO
Human-Object Interaction Detection
Knowledge Distillation
+2
Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training
In this paper, we explore how the 2D modality can benefit 3D masked autoencoding, and propose Joint-MAE, a 2D-3D joint MAE framework for self-supervised 3D point cloud pre-training.
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification.
Ranked #4 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
Training-free 3D Point Cloud Classification
Transfer Learning
+1
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention recently for its transferable visual representation learning.
