Search Results for author:
Found 21 papers, 16 papers with code
FaceNet: A Unified Embedding for Face Recognition and Clustering
On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.
Ranked #1 on
Disguised Face Verification
on MegaFace
Rethinking Atrous Convolution for Semantic Image Segmentation
To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
Ranked #3 on
Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction.
Ranked #85 on
Instance Segmentation
on COCO test-dev
(using extra training data)
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)
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
Modeling Uncertainty with Hedged Instance Embedding
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 val
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on YouTube
Modeling Uncertainty with Hedged Instance Embeddings
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
View-Invariant Probabilistic Embedding for Human Pose
Depictions of similar human body configurations can vary with changing viewpoints.
Ranked #1 on
Pose Retrieval
on MPI-INF-3DHP
EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation.
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people.
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.
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.
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views.
Learning to Generate Image Embeddings with User-level Differential Privacy
Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past.
Unified Visual Relationship Detection with Vision and Language Models
To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs).
Human-Object Interaction Detection
Relationship Detection
+2
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.
Distilling Vision-Language Models on Millions of Videos
Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%.
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
