Search Results for author:
Found 67 papers, 39 papers with code
Colorization of Depth Map via Disentanglement
Vision perception is one of the most important components for a computer or robot to understand the surrounding scene and achieve autonomous applications.
Learning Frame-Wise Emotion Intensity for Audio-Driven Talking-Head Generation
The training signals for this predictor are obtained through our emotion-agnostic intensity pseudo-labeling method without the need of frame-wise intensity labeling.
Self-training Room Layout Estimation via Geometry-aware Ray-casting
In this paper, we introduce a novel geometry-aware self-training framework for room layout estimation models on unseen scenes with unlabeled data.
Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts
Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing.
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models.
PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection
Recent temporal LiDAR-based 3D object detectors achieve promising performance based on the two-stage proposal-based approach.
Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance
Finally, the training-level constraint is utilized by producing accurate and consistent 3D pseudo-labels that align with the visual data.
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
Semi-supervised object detection is crucial for 3D scene understanding, efficiently addressing the limitation of acquiring large-scale 3D bounding box annotations.
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic Scenes
In this paper, we introduce Action-slot, a slot attention-based approach that learns visual action-centric representations, capturing both motion and contextual information.
Text-Driven Image Editing via Learnable Regions
Language has emerged as a natural interface for image editing.
Editing 3D Scenes via Text Prompts without Retraining
To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining.
Delving into Motion-Aware Matching for Monocular 3D Object Tracking
In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking, which is less explored in existing monocular-based approaches.
Multimodal Prompting with Missing Modalities for Visual Recognition
In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models.
Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
In addition, since ground truth annotations are not available during training nor in testing, we leverage the entropy information in multiple layout estimations as a quantitative metric to measure the geometry consistency of the scene, allowing us to evaluate any layout estimator for hyper-parameter tuning, including model selection without ground truth annotations.
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
In this paper, we develop a domain adaptation framework via generating reliable pseudo ground truths of depth from real data to provide direct supervisions.
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation
Thus, state-of-the-art frameworks for monocular 360 depth estimation such as bi-projection fusion in BiFuse are proposed.
Ranked #12 on
Depth Estimation
on Stanford2D3D Panoramic
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
On Generalizing Beyond Domains in Cross-Domain Continual Learning
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
First, a gradient conflict in training due to mismatched label spaces is identified and a class-independent binary cross-entropy loss is proposed to alleviate such label conflicts.
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement
Self-supervised learning on point clouds has gained a lot of attention recently, since it addresses the label-efficiency and domain-gap problems on point cloud tasks.
360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation
We present 360-DFPE, a sequential floor plan estimation method that directly takes 360-images as input without relying on active sensors or 3D information.
Semi-supervised Multi-task Learning for Semantics and Depth
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
Learning to Learn across Diverse Data Biases in Deep Face Recognition
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
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.
Towards Interpretable Deep Networks for Monocular Depth Estimation
Based on our observations, we quantify the interpretability of a deep MDE network by the depth selectivity of its hidden units.
End-to-end Multi-modal Video Temporal Grounding
Specifically, we adopt RGB images for appearance, optical flow for motion, and depth maps for image structure.
LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering
Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space.
Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images
This paper presents a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i. e., equirectangular images) in spherical projection.
Understanding Synonymous Referring Expressions via Contrastive Features
While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences.
LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space.
3D Room Layouts From A Single RGB Panorama
Depth Estimation
+2
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains.
Dual-Stream Fusion Network for Spatiotemporal Video Super-Resolution
Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property.
Voting-based Approaches For Differentially Private Federated Learning
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector
A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds.
Object Detection with a Unified Label Space from Multiple Datasets
To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case.
Learning to Caricature via Semantic Shape Transform
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
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.
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.
Domain Adaptive Semantic Segmentation Using Weak Labels
In this work, we propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain.
Regularizing Meta-Learning via Gradient Dropout
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization.
LayoutMP3D: Layout Annotation of Matterport3D
Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e. g., scene understanding and navigation).
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images.
Ranked #3 on
Omnnidirectional Stereo Depth Estimation
on Helvipad
Progressive Domain Adaptation for Object Detection
This intermediate domain is constructed by translating the source images to mimic the ones in the target domain.
Referring Expression Object Segmentation with Caption-Aware Consistency
To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains.
Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation.
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
In this paper, we propose a single and principled network to jointly learn spatiotemporal correspondence for stereo matching and flow estimation, with a newly designed geometric connection as the unsupervised signal for temporally adjacent stereo pairs.
Weakly-supervised Caricature Face Parsing through Domain Adaptation
However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive.
Domain Adaptation for Structured Output via Disentangled Patch Representations
To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space.
Active Adversarial Domain Adaptation
Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains.
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.
Domain Adaptation for Structured Output via Discriminative Patch Representations
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
Ranked #22 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Unseen Object Segmentation in Videos via Transferable Representations
The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video.
Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation
We propose a novel plug-and-play (PnP) module for improving depth prediction with taking arbitrary patterns of sparse depths as input.
Learning to Adapt Structured Output Space for Semantic Segmentation
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Ranked #3 on
Domain Adaptation
on Synscapes-to-Cityscapes
Adversarial Learning for Semi-Supervised Semantic Segmentation
We propose a method for semi-supervised semantic segmentation using an adversarial network.
Learning Video-Story Composition via Recurrent Neural Network
In this paper, we propose a learning-based method to compose a video-story from a group of video clips that describe an activity or experience.
Learning Binary Residual Representations for Domain-specific Video Streaming
Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission.
Scene Parsing with Global Context Embedding
We present a scene parsing method that utilizes global context information based on both the parametric and non- parametric models.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos.
Ranked #67 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Learning to Segment Instances in Videos with Spatial Propagation Network
In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video.
Video Segmentation via Object Flow
Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds.
Ranked #74 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
(using extra training data)
Adaptive Region Pooling for Object Detection
Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity.
