Search Results for author:
Found 24 papers, 9 papers with code
Video sentence grounding with temporally global textual knowledge
Temporal sentence grounding involves the retrieval of a video moment with a natural language query.
Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring
Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems.
Learning-Based Biharmonic Augmentation for Point Cloud Classification
Point cloud datasets often suffer from inadequate sample sizes in comparison to image datasets, making data augmentation challenging.
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment.
OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple Pedestrian Tracking
Particularly, the improvements on IDF1, IDSw, AssA, and AssR demonstrate the effectiveness of our OccluTrack on tracking and association performance.
Top-Down Framework for Weakly-supervised Grounded Image Captioning
Weakly-supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision.
SSN: Stockwell Scattering Network for SAR Image Change Detection
Recently, synthetic aperture radar (SAR) image change detection has become an interesting yet challenging direction due to the presence of speckle noise.
A Byte Sequence is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings
File fragment classification (FFC) on small chunks of memory is essential in memory forensics and Internet security.
Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration
In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream.
TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervision
We conduct extensive experiments to analyze each of our proposed components and show the efficacy of our framework in generating high-fidelity 3D textured and text-relevant shapes.
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision
More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds
While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler and cheaper labels.
Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set Classification
Recent image classification algorithms, by learning deep features from large-scale datasets, have achieved significantly better results comparing to the classic feature-based approaches.
Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories.
Empirical Analysis of Overfitting and Mode Drop in GAN Training
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective.
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network.
AANet: Attribute Attention Network for Person Re-Identifications
This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem.
Semantic Granularity Metric Learning for Visual Search
In view of this, we propose a new deep semantic granularity metric learning (SGML) that develops a novel idea of leveraging attribute semantic space to capture different granularity of similarity, and then integrate this information into deep metric learning.
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.
DCI: Discriminative and Contrast Invertible Descriptor
In this paper, we proposed a Discriminative and Contrast Invertible (DCI) local feature descriptor.
Interest Point Detection based on Adaptive Ternary Coding
In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism.
The Unusual Effectiveness of Averaging in GAN Training
We examine two different techniques for parameter averaging in GAN training.
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy.
Autoregressive Generative Adversarial Networks
Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones.
