Search Results for author:
Found 37 papers, 23 papers with code
IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic
Intelligent vehicle systems require a deep understanding of the interplay between road conditions, surrounding entities, and the ego vehicle's driving behavior for safe and efficient navigation.
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task.
Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Action Generation
We show that utilizing these detailed descriptions instead of the original action phrases leads to better alignment of text and motion spaces.
DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games
Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings.
UAV-based Visual Remote Sensing for Automated Building Inspection
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes.
PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition
At the representation level, we propose a global frame based part stream approach as opposed to conventional modality based streams.
Ranked #13 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles.
Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches
This skew affects all stages within the pipelines of deep crowd counting approaches.
Automatic Quantification and Visualization of Street Trees
We obtain TCDCA of 96. 77% on the test videos, with a remarkable improvement of 22. 58% over baseline, and demonstrate that our counting module's performance is close to human level.
MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion
We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.
MeronymNet: A Hierarchical Approach for Unified and Controllable Multi-Category Object Generation
We introduce MeronymNet, a novel hierarchical approach for controllable, part-based generation of multi-category objects using a single unified model.
F3: Fair and Federated Face Attribute Classification with Heterogeneous Data
F3 adopts multiple heuristics to improve fairness across different demographic groups without requiring data homogeneity assumption.
Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts
Handwritten documents are often characterized by dense and uneven layout.
BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation
Precise boundary annotations of image regions can be crucial for downstream applications which rely on region-class semantics.
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting
We analyze the performance of representative crowd counting approaches across standard datasets at per strata level and in aggregate.
Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization
State of the art architectures for untrimmed video Temporal Action Localization (TAL) have only considered RGB and Flow modalities, leaving the information-rich audio modality totally unexploited.
Ranked #1 on
Temporal Action Localization
on THUMOS'14
Monocular Multi-Layer Layout Estimation for Warehouse Racks
Given a monocular colour image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as multi-layer layout prediction.
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions
The lack of fine-grained joints (facial joints, hand fingers) is a fundamental performance bottleneck for state of the art skeleton action recognition models.
Ranked #1 on
Skeleton Based Action Recognition
on NTU60-X
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
We deploy SynSE for the task of skeleton-based action sequence recognition.
Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments
In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learned features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence, and pose graph submodules of the SLAM pipeline.
Quo Vadis, Skeleton Action Recognition ?
To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset.
Ranked #1 on
Skeleton Based Action Recognition
on Skeletics-152
OPAL-Net: A Generative Model for Part-based Object Layout Generation
We propose OPAL-Net, a novel hierarchical architecture for part-based layout generation of objects from multiple categories using a single unified model.
Topological Mapping for Manhattan-like Repetitive Environments
At the intermediate level, the map is represented as a Manhattan Graph where the nodes and edges are characterized by Manhattan properties and as a Pose Graph at the lower-most level of detail.
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts.
Instance Segmentation
Optical Character Recognition (OCR)
+1
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification
Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras.
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered a marker for gauging progress in Computer Vision.
SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks
We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities.
Object category understanding via eye fixations on freehand sketches
In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories.
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail.
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
In our work, we propose a recurrent neural network architecture for sketch object recognition which exploits the long-term sequential and structural regularities in stroke data in a scalable manner.
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Current state of the art object recognition architectures achieve impressive performance but are typically specialized for a single depictive style (e. g. photos only, sketches only).
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective.
Analyzing structural characteristics of object category representations from their semantic-part distributions
Studies from neuroscience show that part-mapping computations are employed by human visual system in the process of object recognition.
Expresso : A user-friendly GUI for Designing, Training and Exploring Convolutional Neural Networks
With a view to provide a user-friendly interface for designing, training and developing deep learning frameworks, we have developed Expresso, a GUI tool written in Python.
Freehand Sketch Recognition Using Deep Features
Therefore, analyzing such sparse sketches can aid our understanding of the neuro-cognitive processes involved in visual representation and recognition.
Category-Epitomes : Discriminatively Minimalist Representations for Object Categories
Freehand line sketches are an interesting and unique form of visual representation.
