Search Results for author:
Found 38 papers, 14 papers with code
Learning from Models and Data for Visual Grounding
We introduce SynGround, a novel framework that combines data-driven learning and knowledge transfer from various large-scale pretrained models to enhance the visual grounding capabilities of a pretrained vision-and-language model.
Improved Visual Grounding through Self-Consistent Explanations
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image.
Joint Depth Prediction and Semantic Segmentation with Multi-View SAM
Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images.
Segment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.
Ranked #2 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
Point-Level Region Contrast for Object Detection Pre-Training
In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection.
Neural Pseudo-Label Optimism for the Bank Loan Problem
The lender only observes whether a customer will repay a loan if the loan is issued to begin with, and thus modeled decisions affect what data is available to the lender for future decisions.
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects.
Worldsheet: Wrapping the World in a 3D Sheet for View Synthesis from a Single Image
We present Worldsheet, a method for novel view synthesis using just a single RGB image as input.
Similarity Search for Efficient Active Learning and Search of Rare Concepts
Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples.
A Mask-RCNN Baseline for Probabilistic Object Detection
The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability-based Detection Quality (PDQ), on a new synthetic image dataset.
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things
In addition, the instance mask projection operator works well on other (non-clothing) datasets, providing an improvement of 3 points in mIOU on Thing classes of Cityscapes, a self-driving dataset, on top of a state-of-the-art approach.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
COCO test-dev results are up to 41. 4 mAP for RetinaMask-101 vs 39. 1mAP for RetinaNet-101, while the runtime is the same during evaluation.
Ranked #154 on
Object Detection
on COCO minival
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Target Driven Instance Detection
While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem.
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers
The meta learning is driven by the goal of deep networks that can quickly be adapted to robustly model a particular target in future frames.
Video Highlight Prediction Using Audience Chat Reactions
Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis.
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion.
A Dataset for Developing and Benchmarking Active Vision
We present a new public dataset with a focus on simulating robotic vision tasks in everyday indoor environments using real imagery.
Synthesizing Training Data for Object Detection in Indoor Scenes
In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection.
DSSD : Deconvolutional Single Shot Detector
The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection.
Combining Multiple Cues for Visual Madlibs Question Answering
This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset.
Fast Single Shot Detection and Pose Estimation
For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection.
When was that made?
Toward this goal, we utilize features from existing deep networks and also fine-tune new networks for temporal estimation.
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset.
Modeling Context in Referring Expressions
Humans refer to objects in their environments all the time, especially in dialogue with other people.
SSD: Single Shot MultiBox Detector
Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.
Ranked #3 on
Object Detection
on PASCAL VOC 2012
Visual Madlibs: Fill in the Blank Description Generation and Question Answering
In this paper, we introduce a new dataset consisting of 360, 001 focused natural language descriptions for 10, 738 images.
Where to Buy It: Matching Street Clothing Photos in Online Shops
In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop.
Learning to decompose for object detection and instance segmentation
Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required.
Piecewise Linear Activation Functions For More Efficient Deep Networks
This submission has been withdrawn by arXiv administrators because it is intentionally incomplete, which is in violation of our policies.
ParseNet: Looking Wider to See Better
When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines.
Ranked #39 on
Semantic Segmentation
on PASCAL VOC 2012 test
PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion
Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification.
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.
Visual Madlibs: Fill in the blank Image Generation and Question Answering
In this paper, we introduce a new dataset consisting of 360, 001 focused natural language descriptions for 10, 738 images.
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition
We present a novel approach to efficiently learn a label tree for large scale classification with many classes.
