Search Results for author:
Found 19 papers, 7 papers with code
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Autonomous driving progress relies on large-scale annotated datasets.
Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks
Visible (VIS) imagery of satellites has various important applications in meteorology, including monitoring Tropical Cyclones (TCs).
Stream Query Denoising for Vectorized HD Map Construction
This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction.
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.
ADriver-I: A General World Model for Autonomous Driving
Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I.
VLM-Eval: A General Evaluation on Video Large Language Models
Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent.
Filler Word Detection with Hard Category Mining and Inter-Category Focal Loss
Filler words like ``um" or ``uh" are common in spontaneous speech.
Streaming Video Model
We believe that the concept of streaming video model and the implementation of S-ViT are solid steps towards a unified deep learning architecture for video understanding.
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.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on Long Video Dataset
(using extra training data)
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 #4 on
Cross-Modal Retrieval
on Flickr30k
(using extra training data)
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.
When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
It can be even replaced by a zero-parameter operation.
Ranked #67 on
Object Detection
on COCO minival
(APM metric)
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.
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 #391 on
Image Classification
on ImageNet
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.
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications.
Ranked #873 on
Image Classification
on ImageNet
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.
