Search Results for author:
Found 33 papers, 9 papers with code
Large Scale Visual Recommendations From Street Fashion Images
We describe a completely automated large scale visual recommendation system for fashion.
Geometric VLAD for Large Scale Image Search
We present a novel compact image descriptor for large scale image search.
Region-based Discriminative Feature Pooling for Scene Text Recognition
We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition.
When relevance is not Enough: Promoting Visual Attractiveness for Fashion E-commerce
This study aims to address the effectiveness of types of image in showcasing fashion apparel in terms of its attractiveness, i. e. the ability to draw consumer's attention, interest, and in return their engagement.
Human-Computer Interaction K.4.4; H.2.8
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
Ranked #174 on
Image Classification
on CIFAR-100
Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer Depth Camera and Synthetic Data
In this paper, we propose a method for capturing accurate human body shape and anthropometrics from a single consumer grade depth sensor.
ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories.
Efficient Media Retrieval from Non-Cooperative Queries
Text is ubiquitous in the artificial world and easily attainable when it comes to book title and author names.
Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors
In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying.
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
GraB: Visual Saliency via Novel Graph Model and Background Priors
We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors.
Visual Search at eBay
We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale.
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks.
Conditional Image-Text Embedding Networks
This paper presents an approach for grounding phrases in images which jointly learns multiple text-conditioned embeddings in a single end-to-end model.
ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
Understanding clothes from a single image has strong commercial and cultural impacts on modern societies.
Adversarial Learning for Fine-grained Image Search
Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories.
Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback
In this paper, we introduce an attribute-based interactive image search which can leverage human-in-the-loop feedback to iteratively refine image search results.
Brand > Logo: Visual Analysis of Fashion Brands
In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands.
Understanding Image Quality and Trust in Peer-to-Peer Marketplaces
In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces.
Mobile Head Tracking for eCommerce and Beyond
Unlike desktop and laptop computers, they are also much easier to carry indoors and outdoors. To address this, we implement and open source button that is sensitive to head movements tracked from the front camera of iPhone X.
Human-Computer Interaction
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization
Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels.
Weakly-Supervised Semantic Segmentation via Sub-category Exploration
Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions.
Self-Attentive 3D Human Pose and Shape Estimation from Videos
The key insights of our method are two-fold.
Ranked #53 on
3D Human Pose Estimation
on MPI-INF-3DHP
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator
Interactive robots navigating photo-realistic environments need to be trained to effectively leverage and handle the dynamic nature of dialogue in addition to the challenges underlying vision-and-language navigation (VLN).
TEACh: Task-driven Embodied Agents that Chat
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
Physically rearranging objects is an important capability for embodied agents.
Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy
Specifically, we insert a lightweight Feature Compression Module (FeatComp) into a VideoQA model which learns to extract task-specific tiny features as little as 10 bits, which are optimal for answering certain types of questions.
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation
Our results on the coarse-grained instruction following task of REVERIE demonstrate the navigational capability of CLIP, surpassing the supervised baseline in terms of both success rate (SR) and success weighted by path length (SPL).
RREx-BoT: Remote Referring Expressions with a Bag of Tricks
Armed with this intuition, using only a generic vision-language scoring model with minor modifications for 3d encoding and operating in an embodied environment, we demonstrate an absolute performance gain of 9. 84% on remote object grounding above state of the art models for REVERIE and of 5. 04% on FAO.
Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning
In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in solving a task, but can request help from an external expert when needed.
Characterizing Video Question Answering with Sparsified Inputs
From our experiments, we have observed only 5. 2%-5. 8% loss of performance with only 10% of video lengths, which corresponds to 2-4 frames selected from each video.
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer
Regardless of their effectiveness, larger architectures unavoidably prevent the models from being extended to real-world applications, so building a lightweight VL architecture and an efficient learning schema is of great practical value.
"Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations
Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home.
