Search Results for author:
Found 142 papers, 59 papers with code
A Cognitively Motivated Approach to Spatial Information Extraction
We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language.
The First International Ancient Chinese Word Segmentation and POS Tagging Bakeoff: Overview of the EvaHan 2022 Evaluation Campaign
This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LREC 2022).
MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers
This is made possible by utilizing an alternative method for feature transformation to replace the linear projection of fully-connected layers.
Theoretically Guaranteed Distribution Adaptable Learning
It enables the model to effectively track the evolving data distributions.
Precoded faster-than-Nyquist signaling using optimal power allocation for OTFS
A precoded orthogonal time frequency space (OTFS) modulation scheme relying on faster-than-Nyquist (FTN) transmission over doubly selective fading channels is {proposed}, which enhances the spectral efficiency and improves the Doppler resilience.
Stereo-Talker: Audio-driven 3D Human Synthesis with Prior-Guided Mixture-of-Experts
This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body gestures, temporally consistent photo-realistic quality, and continuous viewpoint control.
AuditWen:An Open-Source Large Language Model for Audit
To overcome these challenges, this study introduces AuditWen, an open-source audit LLM by fine-tuning Qwen with constructing instruction data from audit domain.
TeX-NeRF: Neural Radiance Fields from Pseudo-TeX Vision
Therefore, we propose TeX-NeRF, a 3D reconstruction method using only infrared images, which introduces the object material emissivity as a priori, preprocesses the infrared images using Pseudo-TeX vision, and maps the temperatures (T), emissivities (e), and textures (X) of the scene into the saturation (S), hue (H), and value (V) channels of the HSV color space, respectively.
MinerU: An Open-Source Solution for Precise Document Content Extraction
Document content analysis has been a crucial research area in computer vision.
SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views
The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views.
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model
The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning.
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony
For the former, we propose to pre-train on data regarding a fixed identity with neutral emotion, and defer the incorporation of customizable conditions (identity and emotion) to fine-tuning stage, which is boosted by our novel X-Adapter for parameter-efficient fine-tuning.
Optical RISs Improve the Secret Key Rate of Free-Space QKD in HAP-to-UAV Scenarios
This model facilitates adaptive ORIS beam-width control through linear, quadratic, and focusing phase shifts, which are capable of effectively mitigating the detrimental effects of beam broadening and pointing errors (PE).
MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos
We present a novel pipeline for learning high-quality triangular human avatars from multi-view videos.
Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation
We have conducted experiments on Instruct-IPT to demonstrate the effectiveness of our method on manifold tasks, and we have effectively extended our method to diffusion denoisers as well.
Ranked #1 on
Single Image Desnowing
on CSD
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data.
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset.
OpenDataLab: Empowering General Artificial Intelligence with Open Datasets
The dispersion of data sources and diversity of data formats often lead to inefficiencies in data retrieval and processing, significantly impeding the progress of AI research and applications.
Microsaccade-inspired Event Camera for Robotics
These event cameras' output is dependent on both motion and texture.
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation.
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #11 on
Visual Question Answering
on MM-Vet v2
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations.
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
However, the clothing identity uncontrollability and training inefficiency of existing diffusion-based methods, which struggle to maintain the identity even with full parameter training, are significant limitations that hinder the widespread applications.
Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication
Furthermore, we introduce the HoCo holistic communication dataset, which is a valuable resource for future research.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction
Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera.
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio.
Learning to Deblur Polarized Images
However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP.
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX.
Ranked #13 on
Video Question Answering
on MVBench
RAM-Avatar: Real-time Photo-Realistic Avatar from Monocular Videos with Full-body Control
This paper focuses on advancing the applicability of human avatar learning methods by proposing RAM-Avatar which learns a Real-time photo-realistic Avatar that supports full-body control from Monocular videos.
Neural Underwater Scene Representation
Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases.
NB-GTR: Narrow-Band Guided Turbulence Removal
The removal of atmospheric turbulence is crucial for long-distance imaging.
EvDiG: Event-guided Direct and Global Components Separation
In this paper we leverage the event camera to facilitate the separation of direct and global components enabling video-rate separation of high quality.
Image Processing GNN: Breaking Rigidity in Super-Resolution
Alternatively we leverage the flexibility of graphs and propose the Image Processing GNN (IPG) model to break the rigidity that dominates previous SR methods.
Ranked #9 on
Image Super-Resolution
on Urban100 - 4x upscaling
PanGu-$π$: Enhancing Language Model Architectures via Nonlinearity Compensation
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
Towards Higher Ranks via Adversarial Weight Pruning
To this end, we propose a Rank-based PruninG (RPG) method to maintain the ranks of sparse weights in an adversarial manner.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.
Training A Multi-stage Deep Classifier with Feedback Signals
Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons.
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning
By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training.
Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view.
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Ranked #9 on
Visual Question Answering (VQA)
on InfiMM-Eval
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs).
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
To tackle these issues, we introduce MA-FSAR, a framework that employs the Parameter-Efficient Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations.
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world.
GPT4Image: Can Large Pre-trained Models Help Vision Models on Perception Tasks?
We present a new learning paradigm in which the knowledge extracted from large pre-trained models are utilized to help models like CNN and ViT learn enhanced representations and achieve better performance.
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator
More specifically, given a textured face as the source and the rendered face projected from the desired 3DMM coefficients as the target, our proposed Texture-Geometry-aware Diffusion Model decomposes the complex transfer problem into multi-conditional denoising process, where a Texture Attention-based module accurately models the correspondences between appearance and geometry cues contained in source and target conditions, and incorporate extra implicit information for high-fidelity talking face generation.
High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
Specifically, we supplement the emotion style in text prompts and use an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space, which inherits rich semantic prior from CLIP.
Accelerated Distributed Aggregative Optimization
In this paper, we investigate a distributed aggregative optimization problem in a network, where each agent has its own local cost function which depends not only on the local state variable but also on an aggregated function of state variables from all agents.
HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery
To address these issues, this paper presents a novel hybrid inverse kinematics solution, HybrIK, that integrates the merits of 3D keypoint estimation and body mesh recovery in a unified framework.
Ranked #1 on
3D Human Reconstruction
on AGORA
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant.
DPP-based Client Selection for Federated Learning with Non-IID Data
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
The Novel Adaptive Fractional Order Gradient Decent Algorithms Design via Robust Control
The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex.
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading
Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications.
Network Expansion for Practical Training Acceleration
Recently, the sizes of deep neural networks and training datasets both increase drastically to pursue better performance in a practical sense.
1000 FPS HDR Video With a Spike-RGB Hybrid Camera
Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging.
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
The convolutional operation can only capture local information in a window region, which prevents performance from being further improved.
GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts
Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation.
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision.
GotFlow3D: Recurrent Graph Optimal Transport for Learning 3D Flow Motion in Particle Tracking
By introducing a novel deep neural network based on recurrent Graph Optimal Transport, called GotFlow3D, we present an end-to-end solution to learn the 3D fluid flow motion from double-frame particle sets.
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
In edge computing, users' service profiles are migrated due to user mobility.
D&D: Learning Human Dynamics from Dynamic Camera
In this work, we present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera.
Neural Observer with Lyapunov Stability Guarantee for Uncertain Nonlinear Systems
In this paper, we propose a novel nonlinear observer based on neural networks, called neural observer, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems.
NPU-BOLT: A Dataset for Bolt Object Detection in Natural Scene Images
Therefore, the aim of this study is to develop a dataset named NPU-BOLT for bolt object detection in natural scene images and open it to researchers for public use and further development.
Source-Free Domain Adaptation via Distribution Estimation
Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.
Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification
In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data.
Region-Aware Face Swapping
This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction.
PartAfford: Part-level Affordance Discovery from 3D Objects
We propose a novel learning framework for PartAfford, which discovers part-level representations by leveraging only the affordance set supervision and geometric primitive regularization, without dense supervision.
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device.
Learning to dehaze with polarization
Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.
An Image Patch is a Wave: Phase-Aware Vision MLP
To dynamically aggregate tokens, we propose to represent each token as a wave function with two parts, amplitude and phase.
Positive and Unlabeled Federated Learning
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems
Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.
Hire-MLP: Vision MLP via Hierarchical Rearrangement
Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information.
Augmented Shortcuts for Vision Transformers
Transformer models have achieved great progress on computer vision tasks recently.
Federated Learning with Positive and Unlabeled Data
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
Learning Student Networks in the Wild
Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset.
Patch Slimming for Efficient Vision Transformers
We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers.
Ranked #8 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
Universal Adder Neural Networks
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Visibility-aware Trajectory Optimization with Application to Aerial Tracking
The visibility of targets determines performance and even success rate of various applications, such as active slam, exploration, and target tracking.
Trajectory Planning
Robotics
Fast-Tracker 2.0: Improving Autonomy of Aerial Tracking with Active Vision and Human Location Regression
In recent years, several progressive works promote the development of aerial tracking.
Trajectory Planning
Robotics
Manifold Regularized Dynamic Network Pruning
Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure.
FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing
The bottleneck of solving this problem is the accurate perception of rapid dynamic objects.
Motion Compensation
Robust Object Detection
+1
Robotics
Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight
For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages.
Motion Planning
Robotics
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution
To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.
Pre-Trained Image Processing Transformer
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
Ranked #1 on
Single Image Deraining
on Rain100L
(using extra training data)
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.
Ranked #3 on
3D Human Pose Estimation
on EMDB
Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments
In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
SCOP: Scientific Control for Reliable Neural Network Pruning
To increase the reliability of the results, we prefer to have a more rigorous research design by including a scientific control group as an essential part to minimize the effect of all factors except the association between the filter and expected network output.
Searching for Low-Bit Weights in Quantized Neural Networks
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators.
Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode.
EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors
Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction.
Robotics
DTVNet: Dynamic Time-lapse Video Generation via Single Still Image
The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG).
CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller.
Robotics
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
Hierarchical and Efficient Learning for Person Re-Identification
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e. g. model size and latency, which are critical for practical application.
A Semi-Supervised Assessor of Neural Architectures
A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph.
Distilling portable Generative Adversarial Networks for Image Translation
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
On one hand, massive trainable parameters significantly enhance the performance of these deep networks.
Discernible Image Compression
Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
On Positive-Unlabeled Classification in GAN
In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality.
AdderNet: Do We Really Need Multiplications in Deep Learning?
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.
Ranked #29 on
Weakly Supervised Object Detection
on PASCAL VOC 2012 test
(using extra training data)
Learning from Bad Data via Generation
A worst-case formulation can be developed over this distribution set, and then be interpreted as a generation task in an adversarial manner.
CARS: Continuous Evolution for Efficient Neural Architecture Search
Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.
Bringing Giant Neural Networks Down to Earth with Unlabeled Data
Specifically, we exploit the unlabeled data to mimic the classification characteristics of giant networks, so that the original capacity can be preserved nicely.
On the computational complexity of the probabilistic label tree algorithms
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
Multi-View Matrix Completion for Multi-Label Image Classification
Therefore, we propose to weightedly combine the MC outputs of different views, and present the multi-view matrix completion (MVMC) framework for transductive multi-label image classification.
Decomposition-Based Transfer Distance Metric Learning for Image Classification
In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics.
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e. g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e. g. color, texture and shape).
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification
The features used in many image analysis-based applications are frequently of very high dimension.
Cost-Sensitive Feature Selection by Optimizing F-Measures
Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features.
Multi-View Intact Space Learning
In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data.
Data-Free Learning of Student Networks
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
Learning Student Networks via Feature Embedding
Experiments on benchmark datasets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.
Learning Versatile Filters for Efficient Convolutional Neural Networks
A series of secondary filters can be derived from a primary filter.
Robust Student Network Learning
In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network.
Revisiting Perspective Information for Efficient Crowd Counting
Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions.
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines
We propose a training algorithm for this framework to address the different training demands of agent and environment.
AutoEncoder Inspired Unsupervised Feature Selection
High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty.
Beyond Filters: Compact Feature Map for Portable Deep Model
The filter is then re-configured to establish the mapping from original input to the new compact feature map, and the resulting network can preserve intrinsic information of the original network with significantly fewer parameters, which not only decreases the online memory for launching CNN but also accelerates the computation speed.
Towards Evolutional Compression
In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters.
Parametric T-Spline Face Morphable Model for Detailed Fitting in Shape Subspace
As any pre-learnt subspace is not complete to handle the variety and details of faces and expressions, it covers a limited span of morphing.
Privileged Multi-label Learning
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
Deep convolutional neural networks (CNNs) are successfully used in a number of applications.
A Logic of Knowing Why
When we say "I know why he was late", we know not only the fact that he was late, but also an explanation of this fact.
Streaming View Learning
An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed.
Streaming Label Learning for Modeling Labels on the Fly
The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers.
Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing.
B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction
In this paper, B-spline Shape from Motion and Shading (BsSfMS) is proposed to reconstruct continuous B-spline surface for multi-view face images, according to an assumption that shading and motion information in the images contain 1st- and 0th-order derivative of B-spline face respectively.
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.
Bi-objective Optimization for Robust RGB-D Visual Odometry
This paper considers a new bi-objective optimization formulation for robust RGB-D visual odometry.
Local Rademacher Complexity for Multi-label Learning
We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning.
A Survey on Multi-view Learning
Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace.
