Search Results for author:
Found 57 papers, 30 papers with code
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA
In this paper, we first model a confounding effect that causes language and vision bias simultaneously, then propose a counterfactual inference to remove the influence of this effect.
Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map
Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems.
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework.
TG-VQA: Ternary Game of Video Question Answering
Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them.
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
In contrast, we propose a parameter-free temporal shift module that can leverage the spatial U-Net as is for video generation.
RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer
Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy.
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT.
Temporal Segment Transformer for Action Segmentation
To deal with these issues, we propose an attention based approach which we call \textit{temporal segment transformer}, for joint segment relation modeling and denoising.
Dynamic Grained Encoder for Vision Transformers
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer
Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy.
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements.
Learning a Grammar Inducer from Massive Uncurated Instructional Videos
While previous work focuses on building systems for inducing grammars on text that are well-aligned with video content, we investigate the scenario, in which text and video are only in loose correspondence.
Make-A-Video: Text-to-Video Generation without Text-Video Data
We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V).
Ranked #1 on
Text-to-Video Generation
on MSR-VTT
Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority
Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems.
Learning Semantic Correspondence with Sparse Annotations
In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse keypoint annotations.
Expanding Language-Image Pretrained Models for General Video Recognition
Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios.
Ranked #5 on
Zero-Shot Action Recognition
on Kinetics
Rethinking the Evaluation of Unbiased Scene Graph Generation
Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates.
Action Quality Assessment with Temporal Parsing Transformer
Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation
Then, in Pos-NSD, we use a clustering-based algorithm to divide all positive samples into multiple sets, and treat the samples in the noisiest set as noisy positive samples.
Budget-aware Few-shot Learning via Graph Convolutional Network
In this work, we introduce a new budget-aware few-shot learning problem that not only aims to learn novel object categories, but also needs to select informative examples to annotate in order to achieve data efficiency.
SGTR: End-to-end Scene Graph Generation with Transformer
Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property.
Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features.
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer
We develop a decoding-and-assembling paradigm for the end-to-end scene graph generation.
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation
Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans.
Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications
To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP).
Image Processing via Multilayer Graph Spectra
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures.
An EM Framework for Online Incremental Learning of Semantic Segmentation
Incremental learning of semantic segmentation has emerged as a promising strategy for visual scene interpretation in the open- world setting.
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
SAT: 2D Semantics Assisted Training for 3D Visual Grounding
3D visual grounding aims at grounding a natural language description about a 3D scene, usually represented in the form of 3D point clouds, to the targeted object region.
Learning Implicit Temporal Alignment for Few-shot Video Classification
Few-shot video classification aims to learn new video categories with only a few labeled examples, alleviating the burden of costly annotation in real-world applications.
Video-aided Unsupervised Grammar Induction
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video.
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Scene graph generation is an important visual understanding task with a broad range of vision applications.
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition.
Ranked #16 on
Long-tail Learning
on Places-LT
Boundary Proposal Network for Two-Stage Natural Language Video Localization
State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e. g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment.
An Efficient Hypergraph Approach to Robust Point Cloud Resampling
Efficient processing and feature extraction of largescale point clouds are important in related computer vision and cyber-physical systems.
Point Cloud Resampling Through Hypergraph Signal Processing
By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines.
Multi-Scale 2D Temporal Adjacent Networks for Moment Localization with Natural Language
It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video.
Content-based Analysis of the Cultural Differences between TikTok and Douyin
Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention.
Transformer with Bidirectional Decoder for Speech Recognition
To demonstrate our proposed speech transformer with a bidirectional decoder(STBD), we conduct extensive experiments on the AISHELL-1 dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Part-aware Prototype Network for Few-shot Semantic Segmentation
In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation.
Ranked #3 on
Few-Shot Semantic Segmentation
on Pascal5i
Few-Shot Semantic Segmentation
Semi-Supervised Semantic Segmentation
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features.
Global Image Sentiment Transfer
This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer sentiments of images according to a given sentiment tag.
Mi YouTube es Su YouTube? Analyzing the Cultures using YouTube Thumbnails of Popular Videos
In this study, we explore culture preferences among countries using the thumbnails of YouTube trending videos.
Point Cloud Segmentation based on Hypergraph Spectral Clustering
Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis.
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings.
Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language
We address the problem of retrieving a specific moment from an untrimmed video by a query sentence.
Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization
In this report, we introduce the Winner method for HACS Temporal Action Localization Challenge 2019.
An Evaluation of BBR and its variants
The congestion control algorithm bring such importance that it avoids the network link into severe congestion and guarantees network normal operation.
Networking and Internet Architecture
Dynamic Context Correspondence Network for Semantic Alignment
We instantiate our strategy by designing an end-to-end learnable deep network, named as Dynamic Context Correspondence Network (DCCNet).
Exploiting Temporal Relationships in Video Moment Localization with Natural Language
We address the problem of video moment localization with natural language, i. e. localizing a video segment described by a natural language sentence.
Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
Signal processing over graphs has recently attracted significant attentions for dealing with structured data.
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
A promising strategy is to model the feature context by a fully-connected graph neural network (GNN), which augments traditional convolutional features with an estimated non-local context representation.
Congestion Control for RTP Media: a Comparison on Simulated Environment
To develop low latency congestion control algorithm for real time taffic has been gained attention recently.
Networking and Internet Architecture
Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning
However, the conventional deep neural network architecture is limited in learning representations for multi-task RL (MT-RL), as multiple tasks can refer to different kinds of representations.
Predicting Salient Face in Multiple-Face Videos
On the other hand, we find that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces.
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks
RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs.
Ranked #1 on
Skeleton Based Action Recognition
on SBU
