Search Results for author:
Found 16 papers, 15 papers with code
Formal Verification of Markov Processes with Learned Parameters
We introduce the problem of formally verifying properties of Markov processes where the parameters are the output of machine learning models.
VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding.
Ranked #1 on
VCGBench-Diverse
on VideoInstruct
PALO: A Polyglot Large Multimodal Model for 5B People
PALO offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of ~5B people (65% of the world population).
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data.
GLaMM: Pixel Grounding Large Multimodal Model
In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks.
On Orderings of Probability Vectors and Unsupervised Performance Estimation
We conduct numerous experiments on well-known NLP data sets and rigorously explore the performance of different score functions.
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data.
Ranked #5 on
Question Answering
on NExT-QA (Open-ended VideoQA)
VCGBench-Diverse
Video-based Generative Performance Benchmarking (Consistency)
+6
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed.
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks.
Fine-tuned CLIP Models are Efficient Video Learners
Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain.
MaPLe: Multi-modal Prompt Learning
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks.
Ranked #3 on
Prompt Engineering
on Food-101
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision.
Ranked #1 on
Open Vocabulary Object Detection
on OpenImages-v4
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Our EdgeNeXt model with 1. 3M parameters achieves 71. 2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2. 2% with 28% reduction in FLOPs.
Ranked #29 on
Semantic Segmentation
on PASCAL VOC 2012 test
Class-agnostic Object Detection with Multi-modal Transformer
This has been a long-standing question in computer vision.
Self-Supervised Learning for Fine-Grained Visual Categorization
The deconstruction learning forces the model to focus on local object parts, while reconstruction learning helps in learning the correlation between the parts.
Fine-Grained Visual Categorization
Representation Learning
+1
Viability of machine learning to reduce workload in systematic review screenings in the health sciences: a working paper
This shows that machine learning has the potential to significantly revolutionize the abstract screening process in healthcare systematic reviews.
