Search Results for author:
Found 49 papers, 16 papers with code
A Comprehensive Study of Weight Sharing in Graph Networks for 3D Human Pose Estimation
The objective of this paper is to have a comprehensive and systematic study of weight sharing in GCNs for 3D HPE.
Robust Noisy Label Learning via Two-Stream Sample Distillation
Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning.
Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes
To this end, we first devise innovative strategies to adaptively select high-quality positive and negative classes from the label space, by modeling both the confidence and rank of a class in relation to those of the target class.
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model.
Sparse Pedestrian Character Learning for Trajectory Prediction
Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream.
Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking
In this paper, we propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets, so as to achieve robust data association in the tracking process.
Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy
We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems.
Designing a Better Asymmetric VQGAN for StableDiffusion
The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged.
MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking
For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target.
Sparse Instance Conditioned Multimodal Trajectory Prediction
Specifically, SICNet learns comprehensive sparse instances, i. e., representative points of the future trajectory, through a mask generated by a long short-term memory encoder and uses the memory mechanism to store and retrieve such sparse instances.
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition
Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition.
Trajectory Unified Transformer for Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is an essentially connecting link to understanding human behavior.
Learning from Noisy Pseudo Labels for Semi-Supervised Temporal Action Localization
To this end, we propose a unified framework, termed Noisy Pseudo-Label Learning, to handle both location biases and category errors.
Exploring Discrete Diffusion Models for Image Captioning
The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one.
Physical Logic Enhanced Network for Small-Sample Bi-Layer Metallic Tubes Bending Springback Prediction
In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function.
Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization
The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e. g., context and background, in an untrimmed video.
Social Interpretable Tree for Pedestrian Trajectory Prediction
Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications.
Improving robustness of language models from a geometry-aware perspective
On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps.
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks
Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks.
Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence
The intensity pattern of the distorted vortex beam obtained in the experiment is input to the DDNN model, and the predicted phase screen can be used to compensate the distortion in real time.
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Recent work shows that deep neural networks are vulnerable to adversarial examples.
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph
In this manuscript, we introduce a semi-automatic scene graph annotation tool for images, the GeneAnnotator.
Unified Regularity Measures for Sample-wise Learning and Generalization
Fundamental machine learning theory shows that different samples contribute unequally both in learning and testing processes.
Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction
Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area.
Enriching Local and Global Contexts for Temporal Action Localization
We explore two existing models to be the P-Net in our experiments.
Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track
The base model training encourages the model to predict reliable predictions based on single modality (i. e., RGB or optical flow), based on the fusion of which a pseudo ground truth is generated and in turn used as supervision to train the base models.
SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians.
Video Imprint
In the proposed framework, a dedicated feature alignment module is incorporated for redundancy removal across frames to produce the tensor representation, i. e., the video imprint.
Adversarial Attack and Defense in Deep Ranking
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction.
Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context
To address this challenge, we introduce a framework that learns two feature subspaces respectively for actions and their context.
Action Recognition
Weakly-supervised Temporal Action Localization
+1
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization.
Ranked #7 on
Weakly Supervised Action Localization
on THUMOS’14
Video Polyp Segmentation
Weakly Supervised Action Localization
+2
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement
The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i. e. style feature, and the feature representing the invariant semantic content, i. e. content feature.
Practical Relative Order Attack in Deep Ranking
In this paper, we formulate a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order among a selected set of candidates according to an attacker-specified permutation, with limited interference to other unrelated candidates.
Probing quasi-long-range ordering by magnetostriction in monolayer CoPS3
Mermin-Wagner-Coleman theorem predicts no long-range magnetic order at finite temperature in the two-dimensional (2D) isotropic systems, but a quasi-long-range order with a divergent correlation length at the Kosterlitz-Thouless (KT) transition for planar magnets.
Mesoscale and Nanoscale Physics
The Unreasonable Effectiveness of the Class-reversed Sampling in Tail Sample Memorization
Long-tailed visual class recognition poses significant challenges to traditional machine learning and emerging deep networks due to its inherent class imbalance.
Meta Pairwise Relationship Distillation for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) remains challenging due to the lack of ground-truth labels.
Practical Order Attack in Deep Ranking
The objective of this paper is to formalize and practically implement a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order of a selected set of candidates according to a permutation vector predefined by the attacker, with only limited interference to other unrelated candidates.
Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization
Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision.
Ranked #12 on
Weakly Supervised Action Localization
on THUMOS14
Vocal Bursts Valence Prediction
Weakly Supervised Action Localization
+2
Meta Corrupted Pixels Mining for Medical Image Segmentation
However the training of deep neural network requires a large amount of samples with high-quality annotations.
Ghost Handwritten Digit Recognition based on Deep Learning
We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN.
Adversarial Ranking Attack and Defense
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
Ladder Loss for Coherent Visual-Semantic Embedding
For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way -- relevant or irrelevant, and all "irrelevant" candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query.
Bimodal Stereo: Joint Shape and Pose Estimation from Color-Depth Image Pair
In the pipeline, the estimated shape is refined by the shape prior from the given depth map under the estimated pose.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.
Ranked #9 on
Ad-Hoc Information Retrieval
on TREC Robust04
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs).
Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding
We address the problem of dense visual-semantic embedding that maps not only full sentences and whole images but also phrases within sentences and salient regions within images into a multimodal embedding space.
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting
We develop a unified framework for complex event retrieval, recognition and recounting.
Ordinal Regression With Multiple Output CNN for Age Estimation
To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem.
