Search Results for author:
Found 31 papers, 19 papers with code
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community.
Ranked #1 on
Object Detection
on COCO
kMaX-DeepLab: k-means Mask Transformer
However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features.
Ranked #2 on
Panoptic Segmentation
on COCO test-dev
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering.
Ranked #6 on
Panoptic Segmentation
on COCO test-dev
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
Rethinking Deep Face Restoration
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.
Federated Multi-Target Domain Adaptation
We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data).
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
BasisNet: Two-stage Model Synthesis for Efficient Inference
With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80. 0% on ImageNet.
Ranked #621 on
Image Classification
on ImageNet
Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data
This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories.
STEP: Segmenting and Tracking Every Pixel
The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation.
A Flexible Framework for Discovering Novel Categories with Contrastive Learning
This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories.
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public.
Ranked #1 on
Video Panoptic Segmentation
on Cityscapes-VPS
(using extra training data)
Depth-aware Video Panoptic Segmentation
Monocular Depth Estimation
+2
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
As a result, MaX-DeepLab shows a significant 7. 1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time.
Ranked #11 on
Panoptic Segmentation
on COCO test-dev
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.
Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze
In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D gaze estimation task during the training phase.
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions.
Ranked #4 on
Panoptic Segmentation
on Cityscapes val
(using extra training data)
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.
Ranked #3 on
Instance Segmentation
on Cityscapes test
(using extra training data)
Search to Distill: Pearls are Everywhere but not the Eyes
Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture.
Panoptic-DeepLab
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.
SPGNet: Semantic Prediction Guidance for Scene Parsing
The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.
Searching for MobileNetV3
We achieve new state of the art results for mobile classification, detection and segmentation.
Ranked #4 on
Dichotomous Image Segmentation
on DIS-TE3
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing.
Ranked #32 on
Panoptic Segmentation
on Cityscapes val
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Ranked #1 on
Human Part Segmentation
on PASCAL-Person-Part
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Spatially Adaptive Computation Time for Residual Networks
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
We then exploit a CNN on top of these proposals to perform object detection.
MovieQA: Understanding Stories in Movies through Question-Answering
We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text.
3D Object Proposals for Accurate Object Class Detection
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
Skip-Thought Vectors
The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.
Ranked #2 on
Semantic Similarity
on SICK
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection.
