Search Results for author:
Found 38 papers, 9 papers with code
Bidirectional Alignment for Domain Adaptive Detection with Transformers
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance.
Markov Game Video Augmentation for Action Segmentation
Our key novelty is that we augment the original training videos in the deep feature space, not in the visual spatiotemporal domain as done by previous work.
iFS-RCNN: An Incremental Few-shot Instance Segmenter
This paper addresses incremental few-shot instance segmentation, where a few examples of new object classes arrive when access to training examples of old classes is not available anymore, and the goal is to perform well on both old and new classes.
DESTR: Object Detection With Split Transformer
Second, we use a mini-detector to initialize the content queries in the decoder with classification and regression embeddings of the respective heads in the mini-detector.
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user.
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images.
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation
This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video.
Action Shuffle Alternating Learning for Unsupervised Action Segmentation
Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding.
FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter
This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes.
A Self-supervised GAN for Unsupervised Few-shot Object Recognition
This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest.
Set-Constrained Viterbi for Set-Supervised Action Segmentation
This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering.
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
We specify two new optimization algorithms: Cayley SGD with momentum, and Cayley ADAM on the Stiefel manifold.
Weakly Supervised Energy-Based Learning for Action Segmentation
This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown.
Feature Weighting and Boosting for Few-Shot Segmentation
Finally, the target object is segmented in the query image by using a cosine similarity between the class feature vector and the query's feature map.
Ranked #74 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust
We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations.
Action Recognition
Explainable Artificial Intelligence (XAI)
+2
Natural Language Interaction with Explainable AI Models
This paper presents an explainable AI (XAI) system that provides explanations for its predictions.
Temporal Deformable Residual Networks for Action Segmentation in Videos
This paper is about temporal segmentation of human actions in videos.
Ranked #24 on
Action Segmentation
on GTEA
Unsupervised Video Summarization With Adversarial LSTM Networks
The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video.
Budget-Aware Deep Semantic Video Segmentation
In this work, we study a poorly understood trade-off between accuracy and runtime costs for deep semantic video segmentation.
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation
This paper addresses the problem of weakly supervised semantic image segmentation.
Fine-Grained Recognition as HSnet Search for Informative Image Parts
This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN).
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition
This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities.
Ranked #12 on
Group Activity Recognition
on Volleyball
Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion
Using deep learning, this paper addresses the problem of joint object boundary detection and boundary motion estimation in videos, which we named boundary flow estimation.
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Each state of the beam search corresponds to a candidate CNN.
Approximate Policy Iteration for Budgeted Semantic Video Segmentation
Our second contribution is the algorithm for learning a policy for the sparse selection of supervoxels and their descriptors for budgeted CRF inference.
Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction
This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces.
Monocular Depth Estimation Using Neural Regression Forest
This paper presents a novel deep architecture, called neural regression forest (NRF), for depth estimation from a single image.
Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition
This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data -- namely, 3D human-skeleton sequences -- aimed at complementing poorly represented or missing features of human actions in the training videos.
Semantic Segmentation of RGBD Images With Mutex Constraints
In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes.
Tree-Cut for Probabilistic Image Segmentation
This paper presents a new probabilistic generative model for image segmentation, i. e. the task of partitioning an image into homogeneous regions.
HC-Search for Structured Prediction in Computer Vision
The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.
Latent Trees for Estimating Intensity of Facial Action Units
Our model is a latent tree (LT) that represents input features of facial landmark points and FAU intensities as leaf nodes, and encodes their higher-order dependencies with latent nodes at tree levels closer to the root.
Person Count Localization in Videos From Noisy Foreground and Detections
This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections.
Joint Inference of Groups, Events and Human Roles in Aerial Videos
This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events.
Scene Labeling Using Beam Search Under Mutex Constraints
This paper addresses the problem of assigning object class labels to image pixels.
Multi-Object Tracking via Constrained Sequential Labeling
This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations.
Segmentation as Maximum-Weight Independent Set
The algorithm seeks a solution directly in the discrete domain, instead of relaxing MWIS to a continuous problem, as common in previous work.
(RF)^2 -- Random Forest Random Field
We use these class histograms for a non-parametric estimation of the distribution ratios.
