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- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling1,232
Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs.
- EVA: Exploring the Limits of Masked Visual Representation Learning at Scale1,054
Initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models.
- Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks821
It is shown that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning, and reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility.
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- EVA-02: A visual representation for neon genesis608
EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling, is launched, demonstrating superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets.
- Generative Multimodal Models are In-Context Learners545
This work demonstrates that the task-agnostic in-context learning capabilities of large multimodal models can be significantly enhanced by effective scaling-up, and introduces Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences with a unified autoregressive objective.
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- Images Speak in Images: A Generalist Painter for In-Context Visual Learning412
This work presents Painter, a generalist model which addresses obstacles with an “image”-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.
- Bi-Directional Cascade Network for Perceptual Edge Detection362
A Bi-Directional Cascade Network (BDCN) structure is proposed, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs.
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- Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks316
This paper theoretically analyzes ANN-SNN conversion error, and proposes the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs.
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- Learning Open Set Network with Discriminative Reciprocal Points294
A new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category, which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations.
- SegGPT: Segmenting Everything In Context291
SegGPT is presented, a generalist model for segmenting everything in context that accommodates different kinds of segmentation data by transforming them into the same format of images and shows strong capabilities in segmenting in-domain and out-of-domain targets.
- Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding288
This work proposes Video-XL, a novel approach that leverages MLLMs’ inherent key-value (KV) sparsification capacity to condense the visual input, and introduces a new special token, the Visual Summarization Token (VST), for each interval of the video, which summarizes the visual information within the interval as its associated KV.
- Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics271
The definition, formulation, and paradigm of VCM are given, and the preliminary results have demonstrated the performance and efficiency gains, and state-of-the-art techniques in video compression and feature compression are systematically reviewed from the unique perspective of MPEG standardization.
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- Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks208
This paper theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion, and proposes Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN.
- Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning197
A Transductive Episodic-wise Adaptive Metric (TEAM) framework for few-shot learning is proposed, by integrating the meta-learning paradigm with both deep metric learning and transductive inference and leverages an attention-based bi-directional similarity strategy for extracting the more robust relationship between queries and prototypes.
- Multi-Scale 3D Convolution Network for Video Based Person Re-Identification187
Evaluations on three widely used benchmarks datasets, i.e., MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of the proposed two-stream convolution network over existing 3D convolution networks and state-of-art methods.
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- Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video155
This work proposes a multi-task learning algorithm to learn the task-related “stimulus-saliency” mapping functions for each scene, which learns various fusion strategies, which are used to integrate the stimulus-driven and task- related components to obtain the visual saliency.
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- Event-based Video Reconstruction via Potential-assisted Spiking Neural Network140
This paper proposes a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron, and finds that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks.
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- A Retina-Inspired Sampling Method for Visual Texture Reconstruction125
A fovea-like sampling method inspired by the neuron signal processing in retina, which aims at visual texture reconstruction only taking advantage of the properties of spikes, which is capable of changing the way that demanding machine vision applications are built.
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- Ethical Principles and Governance Technology Development of AI in China113
A survey of efforts to develop AI governance technologies in China is presented and the major research challenges in AI governance research are described and future research directions are discussed.
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- Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging105
- Multimodal Deep Convolutional Neural Network for Audio-Visual Emotion Recognition105
This paper proposes to bridge the emotional gap based on a multimodal Deep Convolution Neural Network (DCNN), which fuses the audio and visual cues in a deep model, which is trained with two stages.
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- Cross-media analysis and reasoning: advances and directions104
By presenting approaches, advances, and future directions in cross-media analysis and reasoning, the goal is to draw more attention to the state-of-the-art advances in the field, and to provide technical insights by discussing the challenges and research directions in these areas.
- Vlogging99
A comprehensive survey of videoblogging (vlogging for short) as a new technological trend is presented and several multimedia technologies are introduced to empower vlogging technology with better scalability, interactivity, searchability, and accessability.
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- Diversifying the image retrieval results96
A re-ranking method based on topic richness analysis is proposed to enrich topic coverage in retrieval results and a quantitative criterion called diversity scores (DS) is proposed in order to evaluate the improvement.
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- Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation93
A spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images.
- Spike Camera and Its Coding Methods91
This paper introduces a spike camera with a distinct video capture scheme and proposes two methods of decoding the spike stream for texture reconstruction, making it possible to reconstruct the picture of any moment which enables the playback of high speed movement.
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- Compact Descriptors for Video Analysis: The Emerging MPEG Standard90
The new standard that is being developed and the performance of these key technical contributions are described are described.
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- P-ODN: Prototype-based Open Deep Network for Open Set Recognition84
The prototype-based Open Deep Network (P-ODN) is proposed, which introduces prototype learning into open set recognition and achieves state-of-the-art performance on the UCF11,UCF50, UCF101 and HMDB51 datasets.
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- Cross-Domain Adversarial Feature Learning for Sketch Re-identification77
This paper addresses the Sketch Re-ID problem by proposing a cross-domain adversarial feature learning approach to jointly learn the identity features and domain-invariant features and shows that the proposed method outperforms the state-of-the-arts.
- Incorporating intra-class variance to fine-grained visual recognition73
The triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network.
- Robust multiple cameras pedestrian detection with multi-view Bayesian network71
A robust multiple cameras pedestrian detection approach with multi-view Bayesian network model (MvBN) that outperforms several state-of-the-art algorithms remarkably and demonstrates high robustness in different surveillance scenes is proposed.
- Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network69
This paper proposes a biologicallyinspired deep network, called shuttleNet, which can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.
- Visual Saliency with Statistical Priors69
In this paper, the bottom-up saliency is calculated to pop-out the visual subsets that are probably salient, while the prior knowledge is used to recover the wrongly suppressed targets and inhibit the improperly popped-out distractors.
- Driver Emotion Recognition With a Hybrid Attentional Multimodal Fusion Framework68
This work proposes to recognize driver emotion using a novel multimodal fusion framework based on convolutional long-short term memory network (ConvLSTM), and hybrid attention mechanism to fuse non-invasive multimodAL data of eye, vehicle, and environment.
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- Imitating the brain with neurocomputer a “new” way towards artificial general intelligence65
The neurocomputer is compared with the conventional computer and the philosophy under the “new” approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded.
- CNN vs. SIFT for Image Retrieval65
This work proposes an image representation model, complementary CNN and SIFT (CCS), to fuse CNN andSIFT in a multi-level and complementary way, and experimental results show that the CCS achieves state-of-the-art retrieval results.
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- Spike Transformer: Monocular Depth Estimation for Spiking Camera64
This paper presents Spike Transformer (Spike-T), a novel paradigm for learning spike data and estimating monocular depth from continuous spike streams, and is the first time that directly depth estimation from spike streams becomes possible.
- Optical Flow Estimation for Spiking Camera64
SCFlow, a tailored deep learning pipeline to estimate optical flow in high-speed scenes from spike streams which can adaptively remove the motion blur in spike streams according to the prior motion is presented.
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- An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification59
An attention-driven two-stage clustering (ADTC) method that uses an unsupervised attention kernel to shift the learned features from the image background to the pedestrian foreground, which results in more informative clusters.
- Reconstruction of natural visual scenes from neural spikes with deep neural networks59
A novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes from experimentally recorded spikes of a population of retinal ganglion cells, and sheds new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.
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- Automatic Webcam-Based Human Heart Rate Measurements Using Laplacian Eigenmap57
A novel webcam-based heart rate measurement method using Laplacian Eigenmap (LE), which obtains higher degrees of agreement with measurements using finger blood oximetry than Independent Component Analysis (ICA), Principal Component analysis (PCA), and other five alternative methods.
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- A new method to segment playfield and its applications in match analysis in sports video51
A novel playfield segmentation method based on Gaussian mixture models (GMMs) is proposed, which is robust to various sports videos even for very poor grass field conditions and match situation analysis is investigated.
- An efficient coding scheme for surveillance videos captured by stationary cameras50
A sequence structure is proposed to generate high qualitybackground frames and efficiently code difference frames without delay, and then surveillance videos can be easily compressed by encoding the background frames and difference frames in a traditional manner.
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- An Efficient Coding Method for Spike Camera Using Inter-Spike Intervals48
This work proposes an intensity-based measurement for spike train distance and designs an efficient coding method to meet the challenge, by investigating the spatiotemporal distribution of the spikes.
- Guest Editorial Special Issue on IoT on the Move: Enabling Technologies and Driving Applications for Internet of Intelligent Vehicles (IoIV)48
As a unique subset of general purpose IoT, IoIV can benefit from the existing research on VANET, which lays the foundation toward a more pervasive and ubiquitous communications and networking core that is essential for IoIV.
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- Comprehensive profiling of Epstein-Barr virus-encoded miRNA species associated with specific latency types in tumor cells48
These data provide a comprehensive profiling of the EBV miRNA transcriptome that is associated with specific tumor cells in the three types of latent EBV infection states and clearly distinguished between the three distinct EBV latency types.
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- Towards human-like and transhuman perception in AI 2.0: a review46
This paper briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines and envision several R&D trends in intelligent perception for the forthcoming era of AI 2.0.
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- Joint Learning of Semantic and Latent Attributes44
It is argued that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation for object recognition but also helps with semantic attribute prediction.
- Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes44
This work proposes a novel approach to learn complementary saliency priors for foreground object segmentation in complex scenes that outperforms 16 state-of-the-art methods remarkably on three public image benchmarks.
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- Automatic interesting object extraction from images using complementary saliency maps41
This paper proposes a novel object extraction approach by integrating two kinds of "complementary" saliency maps, i.e., sketch-like and envelope-like maps, which outperforms six state-of-art saliency-based methods remarkably in automatic object extraction, and is even comparable to some interactive approaches.
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- A GPU-based computational framework that bridges neuron simulation and artificial intelligence38
A novel Dendritic Hierarchical Scheduling (DHS) method that performs at 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform is presented.
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- Brain-inspired artificial intelligence research: A review35
The role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr is reviewed, and the limitations of the current development of AI are explored and analyzed.
- SAP: Self-Adaptive Proposal Model for Temporal Action Detection Based on Reinforcement Learning35
A Self-Adaptive Proposal (SAP) model that learns to find actions through continuously adjusting the temporal bounds in a self-adaptive way is proposed, which can achieve competitive performance with current action detection algorithms via much fewer proposals.
- Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection33
This work proposes a Self-Guided Adaptation model, targeting at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty, and shows that SGA improves the state-of-the-art methods with significant margins especially on large domain shift cases.
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- From Part to Whole31
A multi-task multi-range (MTMR) representation framework is proposed which is composed of local, regional and global representations and attempts to optimize authorship identification with subtly correlated identification tasks such as style, genre and date.
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- UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging27
This paper reformulates the modulo image unwrapping problem into a series of binary labeling problems and proposes a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping.
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- Multimodal large language models for bioimage analysis26
An overview of multimodal large language models through the lens of bioimage analysis is given and how these models could be built as a community to facilitate biology research is discussed.
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- A background proportion adaptive Lagrange multiplier selection method for surveillance video on HEVC23
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- Hamming compatible quantization for hashing21
This paper proposes a multi-bit quantization method named Hamming Compatible Quantization (HCQ) to preserve the capability of similarity metric between Euclidean space and Hamming space by utilizing the neighborhood structure of raw data.
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- Free-view gait recognition18
The proposed view-adaptive mapping (VAM) approach employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation.
- Content-based copy detection through multimodal feature representation and temporal pyramid matching18
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- From Data to Knowledge17
The deep learning model compression is investigated and its promise use is demonstrated through a set of experiments and the necessity, importance and open problems regarding the standardization of deep learning models, which enables the interoperability with the standardized compact model representation bitstream syntax, are discussed.
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- Learning Compact Visual Descriptors for Low Bit Rate Mobile Landmark Search13
This article introduces the work on low bit rate mobile landmark search, in which a compact yet discriminative landmark image descriptor is extracted by using location context such as GPS, crowd-sourced hotspot WLAN, and cell tower locations.
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- Pushing the Limits of 3D Shape Generation at Scale12
This work developed a remarkable model with an astounding 3.6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D, and introduces a discrete codebook for efficient quantization of these representations.
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- Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability11
This paper extends auto-regressive models to 3D domains, and introduces discrete representation learning based on a latent vector instead of volumetric grids, which not only reduces computational costs but also preserves essential geometric details by learning the joint distributions in a more tractable order.
- Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer11
This work proposes a cross-modality cross-domain (BiCross) framework to realize unsupervised spike depth estimation by introducing simulated mediate source spike data, and designs a Coarse-to-Fine Knowledge Distillation (CFKD) approach to facilitate comprehensive cross-modality knowledge transfer.
- Light Flickering Guided Reflection Removal11
This paper proposes a learning-based method that utilizes short-term and long-term observations of mixture videos to exploit one-side contextual clues in fluctuant components and brightness-consistent clues in consistent components for achieving layer separation and flickering removal, respectively.
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- Implicit neural image field for biological microscopy image compression10
This study presents a flexible AI-based method for compressing microscopy images, achieving high compression while preserving details critical for analysis, with support for task-specific optimization and arbitrary-resolution decompression.
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- Affinity Preserving Quantization for Hashing: A Vector Quantization Approach to Learning Compact Binary Codes10
A novel vector quantization method named affinity preserving quantization (APQ) is proposed to improve the quantization quality of projection values, which has significantly boosted the performance of state-of-the-art hashing techniques.
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- SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera7
This study introduces a new method that uses the spike camera to overcome limitations of event-RGB approaches, and designs the Texture from Spike (TfS) loss, which handles foreground objects with backgrounds simultaneously.
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- AI Deception: Risks, Dynamics, and Controls6
This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations, and organizes the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
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- When Visible Light (Backscatter) Communication Meets Neuromorphic Cameras in V2X6
This paper proposes NeuromorphicVLC, a visible light communication system equipped with neuromorphic cameras as optical receivers to improve its performance, and develops a complete signal processing pipeline to detect the VLC transmitters and demodulate the messages.
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- Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics4
This study investigates the compatibility of continuous attractor neural networks (CANNs) and E-INNs and finds that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics.
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- Uncertainty Guided Depth Fusion for Spike Camera3
A novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera to achieve state-of-the-art results on CitySpike20K, surpassing all monocular or stereo spike depth estimation baselines.
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- Improving object detection with region similarity learning2
The proposed triplet embedding has improved the performance of well-known Fas-tRCNN model with a mAP gain of 2.1%.
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- Learning from mobile contexts to minimize the mobile location search latency2
This work combines location related side information from the mobile devices to adaptively supervise the compact visual descriptor design in a flexible manner, which is very suitable to search locations or landmarks within a bandwidth constraint wireless link.
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- Toward a distributed terabyte text retrieval system in China-US million book digital library2
A distributed terabyte text retrieval system for China-US Million Book Digital Library is developed, which can search less data while maintaining acceptable retrieval accuracy, and some new retrieval models and text clustering approaches are explored.
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- HUB: Guiding Learned Optimizers with Continuous Prompt Tuning1
This work proposes a hybrid-update-based (HUB) optimization strategy inspired by recent advancements in hard prompt tuning and result selection techniques used in large language and vision models that is able to retain the benefits of learned optimizers while stabilizing the training process and improving testing performance.
- Spike Stream Denoising via Spike Camera Simulation1
A systematic noise model for spike camera based on its unique circuit is proposed and a tailored spike stream denoising framework (DnSS) where denoised spike stream is obtained by decoding inferred inter-spike intervals is designed.
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- A Cache-Based Distributed Terabyte Text Retrieval System in CADAL1
A cache-based, distributed terabyte text retrieval system, with fulltext retrieval, distributed computing and caching techniques, for the China-America Digital Academic Library project.
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- Allocating images and selecting image collections for distributed visual search–
This paper employs Latent Dirichlet Allocation (LDA) to discover the latent concepts as an intermediate semantic representation over a large scale image database and aims to learn an optimal image allocation for each server node and accurately perform collection selection for each query.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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