Search Results for author:
Found 35 papers, 16 papers with code
Image Quality Assessment for Omnidirectional Cross-reference Stitching
Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with immersive experience.
Cartoon Face Recognition: A Benchmark Dataset
Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications.
Multi-Class Part Parsing With Joint Boundary-Semantic Awareness
In this paper, we propose a joint parsing framework with boundary and semantic awareness to address this challenging problem.
Misalignment Resilient Diffractive Optical Networks
By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN).
Is Depth Really Necessary for Salient Object Detection?
To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information.
Ranked #1 on
RGB-D Salient Object Detection
on RGBD135
Cooperative Bi-path Metric for Few-shot Learning
Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples.
DanceIt: Music-inspired Dancing Video Synthesis
To explore the relationship between music and dance movements, we propose a cross-modal alignment module that focuses on dancing video clips, accompanied on pre-designed music, to learn a system that can judge the consistency between the visual features of pose sequences and the acoustic features of music.
Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.
Learning Predictive Communication by Imagination in Networked System Control
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition
Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects.
Ranked #28 on
Fine-Grained Image Classification
on CUB-200-2011
SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering
To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.
FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning
In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks that are in-sync with the input audio signal.
Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification
Therefore, we propose a Pose-guided inter-and intra-part relational transformer (Pirt) for occluded person Re-Id, which builds part-aware long-term correlations by introducing transformers.
RGB-D Salient Object Detection with Ubiquitous Target Awareness
To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales.
Ranked #10 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Heterogeneous Relational Complement for Vehicle Re-identification
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations.
M3TR: Multi-modal Multi-label Recognition with Transformer
Multi-label image recognition aims to recognize multiple objects simultaneously in one image.
Ranked #4 on
Multi-Label Classification
on PASCAL VOC 2007
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
Ranked #8 on
Multi-Label Classification
on PASCAL VOC 2007
A Structure Feature Algorithm for Multi-modal Forearm Registration
Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education.
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.
Super-resolution image display using diffractive decoders
We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.
Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
Permutation matrices form an important computational building block frequently used in various fields including e. g., communications, information security and data processing.
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition
Recent ideas propose to explore this problem in an unsupervised setting, i. e., without any labels in base classes, which reduces the heavy consumption of manual annotations.
Unidirectional Imaging using Deep Learning-Designed Materials
A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked.
Part-guided Relational Transformers for Fine-grained Visual Recognition
This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing.
Ranked #7 on
Fine-Grained Image Classification
on FGVC Aircraft
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+1
Parsing Objects at a Finer Granularity: A Survey
Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e. g., agriculture, remote sensing, and space technologies.
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL).
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation
Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states.
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles
Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input "difficulty", or user needs.
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes.
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs).
Picking Up Quantization Steps for Compressed Image Classification
The sensitivity of deep neural networks to compressed images hinders their usage in many real applications, which means classification networks may fail just after taking a screenshot and saving it as a compressed file.
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications.
Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets.
High-performance Power Allocation Strategies for Active IRS-aided Wireless Network
Due to its slow linear convergence from iterative GA, the proposed ESMPI-GA is high-complexity.
SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream
Spike cameras, leveraging spike-based integration sampling and high temporal resolution, offer distinct advantages over standard cameras.
