Search Results for author:
Found 22 papers, 11 papers with code
Entity-Level Text-Guided Image Manipulation
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
A noisy training set usually leads to the degradation of the generalization and robustness of neural networks.
Bridged Transformer for Vision and Point Cloud 3D Object Detection
Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises.
SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias
In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias.
A Simple Test-Time Method for Out-of-Distribution Detection
In contrast, this paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Multimodal Token Fusion for Vision Transformers
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Ranked #1 on
Semantic Segmentation
on SUN-RGBD
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
Noisy training set usually leads to the degradation of generalization and robustness of neural networks.
Sound Adversarial Audio-Visual Navigation
In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent.
Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction
For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning.
Ranked #15 on
Semantic Segmentation
on NYU Depth v2
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks
In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts.
Relative Instance Credibility Inference for Learning with Noisy Labels
Specifically, we re-purpose a sparse linear model with incidental parameters as a unified Relative Instance Credibility Inference (RICI) framework, which will detect and remove outliers in the forward pass of each mini-batch and use the remaining instances to train the network.
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network.
Ranked #22 on
Semantic Segmentation
on NYU Depth v2
Elastic Tactile Simulation Towards Tactile-Visual Perception
By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.
Explicit Connection Distillation
In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.
Blind signal decomposition of various word embeddings based on join and individual variance explained
We found that by mapping different word embeddings into the joint component, sentiment performance can be greatly improved for the original word embeddings with lower performance.
Elastic Interaction of Particles for Robotic Tactile Simulation
At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.
Deep Multimodal Fusion by Channel Exchanging
Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity
In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures.
Resolution Switchable Networks for Runtime Efficient Image Recognition
We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances.
Instance Credibility Inference for Few-Shot Learning
To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
