Search Results for author:
Found 24 papers, 11 papers with code
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank.
TridentSE: Guiding Speech Enhancement with 32 Global Tokens
In this paper, we present TridentSE, a novel architecture for speech enhancement, which is capable of efficiently capturing both global information and local details.
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture.
Ranked #1 on
Zero-Shot Video Retrieval
on MSR-VTT
An Anchor-Free Detector for Continuous Speech Keyword Spotting
Continuous Speech Keyword Spotting (CSKWS) is a task to detect predefined keywords in a continuous speech.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody.
Peripheral Vision Transformer
We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data.
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys.
When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
It can be even replaced by a zero-parameter operation.
Ranked #66 on
Object Detection
on COCO minival
(APM metric)
Make It Move: Controllable Image-to-Video Generation with Text Descriptions
With both controllable appearance and motion, TI2V aims at generating videos from a static image and a text description.
Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module.
Ranked #326 on
Image Classification
on ImageNet
Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration
Given a piece of speech and its transcript text, text-based speech editing aims to generate speech that can be seamlessly inserted into the given speech by editing the transcript.
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision.
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
In this paper, we propose a novel contrastive mask prediction (CMP) task for visual representation learning and design a mask contrast (MaskCo) framework to implement the idea.
Unsupervised Visual Representation Learning by Tracking Patches in Video
The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame.
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
Based on the probability space, we further generate new fusion strategies which achieve the state-of-the-art performance on four well-known action recognition datasets.
Tracking by Instance Detection: A Meta-Learning Approach
We propose a principled three-step approach to build a high-performance tracker.
PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
We discover that the two streams should communicate with each other, and this is crucial to phase prediction.
Sound Audio and Speech Processing
Posterior-Guided Neural Architecture Search
Accordingly, a hybrid network representation is presented which enables us to leverage the Variational Dropout so that the approximation of the posterior distribution becomes fully gradient-based and highly efficient.
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking
The two stages are connected in series as the input proposals of the FM stage are generated by the CM stage.
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications.
Towards a Better Match in Siamese Network Based Visual Object Tracker
Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance.
Ranked #9 on
Visual Object Tracking
on VOT2017/18
A Twofold Siamese Network for Real-Time Object Tracking
SA-Siam is composed of a semantic branch and an appearance branch.
Ranked #1 on
Visual Object Tracking
on OTB-50
