Search Results for author:
Found 63 papers, 24 papers with code
The 7th AI City Challenge
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.
Temporal Relevance Analysis for Video Action Models
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.
The 6th AI City Challenge
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
Many-to-many Splatting for Efficient Video Frame Interpolation
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant.
Ranked #1 on
Video Frame Interpolation
on Xiph-4K (Crop)
A Unified Framework for Domain Adaptive Pose Estimation
In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision.
A Broad Study of Pre-training for Domain Generalization and Adaptation
In this paper, we provide a broad study and in-depth analysis of pre-training for domain adaptation and generalization, namely: network architectures, size, pre-training loss, and datasets.
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition.
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
In this paper, we focus on two foundational tasks: the Vehicle Retrieval by NL task and the Vehicle Tracking by NL task, which take advantage of the proposed CityFlow-NL benchmark and provide a strong basis for future research on the multi-target multi-camera tracking by NL description task.
CDS: Cross-Domain Self-Supervised Pre-Training
We present a two-stage pre-training approach that improves the generalization ability of standard single-domain pre-training.
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Recently, most ZSS methods focus on learning the visual-semantic correspondence to transfer knowledge from seen classes to unseen classes at the pixel level.
Self-supervised Visual Attribute Learning for Fashion Compatibility
Our approach consists of three self-supervised tasks designed to capture different concepts that are neglected in prior work that we can select from depending on the needs of our downstream tasks.
Real-time Semantic Segmentation with Fast Attention
The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism and captures the same rich spatial context at a small fraction of the computational cost, by changing the order of operations.
Ranked #32 on
Semantic Segmentation
on DensePASS
Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks
In this work, we develop efficient disruptions of black-box image translation deepfake generation systems.
Temporally Distributed Networks for Fast Video Semantic Segmentation
We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation.
Ranked #2 on
Video Semantic Segmentation
on Cityscapes val
Spatio-Temporal Action Detection with Multi-Object Interaction
Spatio-temporal action detection in videos requires localizing the action both spatially and temporally in the form of an "action tube".
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
We show that when labeled source examples are limited, existing methods often fail to learn discriminative features applicable for both source and target domains.
Explainable Deep Classification Models for Domain Generalization
Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision.
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
This type of manipulated images and video have been coined Deepfakes.
Universal Domain Adaptation through Self Supervision
While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task.
MULE: Multimodal Universal Language Embedding
In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages.
Language Features Matter: Effective Language Representations for Vision-Language Tasks
Shouldn't language and vision features be treated equally in vision-language (VL) tasks?
Real-time Visual Object Tracking with Natural Language Description
In benchmarks, our method is competitive with state of the art trackers, while it outperforms all other trackers on targets with unambiguous and precise language annotations.
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
Learning from a few examples is a challenging task for machine learning.
Multi-way Encoding for Robustness
However, it has been shown that deep models are vulnerable to adversarial examples.
Multi-way Encoding for Robustness to Adversarial Attacks
Deep models are state-of-the-art for many computer vision tasks including image classification and object detection.
Semi-supervised Domain Adaptation via Minimax Entropy
Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.
Revisiting Image-Language Networks for Open-ended Phrase Detection
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image.
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
We consider the problem of fine-grained classification on an edge camera device that has limited power.
Hashing with Binary Matrix Pursuit
We propose theoretical and empirical improvements for two-stage hashing methods.
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
Local Descriptors Optimized for Average Precision
In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval.
Multilevel Language and Vision Integration for Text-to-Clip Retrieval
To capture the inherent structures present in both text and video, we introduce a multilevel model that integrates vision and language features earlier and more tightly than prior work.
Hashing with Mutual Information
Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval.
Excitation Backprop for RNNs
Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks
Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy.
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval.
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems.
MIHash: Online Hashing with Mutual Information
Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data.
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
Image and video analysis is often a crucial step in the study of animal behavior and kinematics.
Top-down Neural Attention by Excitation Backprop
We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps.
Salient Object Subitizing
We study the problem of Salient Object Subitizing, i. e. predicting the existence and the number of salient objects in an image using holistic cues.
Unconstrained Salient Object Detection via Proposal Subset Optimization
Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects.
Learning Activity Progression in LSTMs for Activity Detection and Early Detection
In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection.
Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images.
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web
In contrast, collecting action images from the Web is much easier and training on images requires much less computation.
Ranked #13 on
Action Recognition
on ActivityNet
(using extra training data)
Adaptive Hashing for Fast Similarity Search
With the staggering growth in image and video datasets, algorithms that provide fast similarity search and compact storage are crucial.
Minimum Barrier Salient Object Detection at 80 FPS
Powered by this fast MBD transform algorithm, the proposed salient object detection method runs at 80 FPS, and significantly outperforms previous methods with similar speed on four large benchmark datasets, and achieves comparable or better performance than state-of-the-art methods.
Ranked #6 on
Video Salient Object Detection
on DAVSOD-easy35
(using extra training data)
Online Supervised Hashing for Ever-Growing Datasets
To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets.
A Bayesian Approach for Online Classifier Ensemble
We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble.
Generalized Majorization-Minimization
Majorization-Minimization (MM) is a powerful iterative procedure for optimizing non-convex functions that works by optimizing a sequence of bounds on the function.
Space-Time Tree Ensemble for Action Recognition
Using the action vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of highly discriminative tree patterns.
Class Probability Estimation via Differential Geometric Regularization
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective.
Visual Word Selection without Re-Coding and Re-Pooling
Thus, given a training set for a particular computer vision task, a key problem is pruning a large codebook to select only a subset of visual words.
Detecting Reduplication in Videos of American Sign Language
A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL).
