Search Results for author:
Found 10 papers, 9 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
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
Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models
Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs).
Ranked #3 on
Temporal/Casual QA
on NExT-QA
(using extra training data)
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
Ranked #6 on
Visual Question Answering (VQA)
on InfiMM-Eval
Instruction Following
Optical Character Recognition (OCR)
+7
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
To alleviate this, previous methods simply replace the pixel reconstruction targets of 75% masked tokens by encoded features from pre-trained image-image (DINO) or image-language (CLIP) contrastive learning.
Frozen CLIP Models are Efficient Video Learners
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos.
Ranked #26 on
Action Classification
on Kinetics-400
(using extra training data)
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
This has led to a new research direction in parameter-efficient transfer learning.
Ranked #23 on
Action Recognition
on Something-Something V2
(using extra training data)
ConvMAE: Masked Convolution Meets Masked Autoencoders
Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer architectures can further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and semantic segmentation.
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
