Search Results for author:
Found 18 papers, 7 papers with code
Cubify Anything: Scaling Indoor 3D Object Detection
Furthermore, by pre-training on CA-1M, CuTR can outperform point-based methods on a more diverse variant of SUN RGB-D - supporting the notion that while inductive biases in 3D are useful at the smaller sizes of existing datasets, they fail to scale to the data-rich regime of CA-1M.
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
We present MM1. 5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning.
Ranked #51 on
Visual Question Answering
on MM-Vet
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
As a result, this design allows us to adequately capture both spatial and temporal features that are beneficial for detailed video understanding.
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
A primary objective of alignment for MLLMs is to encourage these models to align responses more closely with image information.
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
In this paper, we expand upon the capabilities of them by training a single model on tens of highly diverse modalities and by performing co-training on large-scale multimodal datasets and text corpora.
4M: Massively Multimodal Masked Modeling
Current machine learning models for vision are often highly specialized and limited to a single modality and task.
GAUDI: A Neural Architect for Immersive 3D Scene Generation
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
It is not only the first RGB-D dataset that is captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released.
License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
This work details Sighthounds fully automated license plate detection and recognition system.
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system.
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks.
Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes
In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently.
Deep Tracking: Visual Tracking Using Deep Convolutional Networks
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking.
Target Identity-Aware Network Flow for Online Multiple Target Tracking
In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously.
GMMCP Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data association is the backbone to many multiple object tracking (MOT) methods.
Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders
Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks.
Ranked #4 on
Kinship Verification
on KinFaceW-II
Improving Semantic Concept Detection through the Dictionary of Visually-distinct Elements
A video captures a sequence and interactions of concepts that can be static, for instance, objects or scenes, or dynamic, such as actions.
Improving an Object Detector and Extracting Regions Using Superpixels
In general, our method takes detection bounding boxes of a generic detector as input and generates the detection output with higher average precision and precise object regions.
