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Found 100 papers, 38 papers with code
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments
We attribute this shortcoming to the scarcity of high-quality datasets in semi-structured scenes, particularly concerning pedestrian perception and prediction.
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC
From the decision-making perspective, to handle complex user instructions and interdependent subtasks more effectively, we propose a hierarchical multi-agent collaboration architecture that decomposes decision-making processes into Instruction-Subtask-Action levels.
Reversing Flow for Image Restoration
Image restoration aims to recover high-quality (HQ) images from degraded low-quality (LQ) ones by reversing the effects of degradation.
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios with mixed or unknown degradations.
mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA
Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge, effectively expanding the knowledge scope.
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality.
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking
Specifically, we propose a Task-specific Hybrid Matching module for a weight-shared cross-attention-based decoder that matches the targets of track queries with multiple object queries to exploit promising candidates overlooked by the self-attention mechanism.
VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization
Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to make the results coherent and realistic.
Token Caching for Diffusion Transformer Acceleration
Diffusion transformers have gained substantial interest in diffusion generative modeling due to their outstanding performance.
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience.
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range.
vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving
This study introduces the vTensor, an innovative tensor structure for LLM inference based on GPU virtual memory management (VMM).
MIBench: Evaluating Multimodal Large Language Models over Multiple Images
However, most existing MLLMs and benchmarks primarily focus on single-image input scenarios, leaving the performance of MLLMs when handling realistic multiple images underexplored.
Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking
Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as auxiliary tasks through embedding Re-Identification task (ReID) into the detector, achieving a balance between inference speed and tracking performance.
Animate3D: Animating Any 3D Model with Multi-view Video Diffusion
The core idea is two-fold: 1) We propose a novel multi-view video diffusion model (MV-VDM) conditioned on multi-view renderings of the static 3D object, which is trained on our presented large-scale multi-view video dataset (MV-Video).
EA-VTR: Event-Aware Video-Text Retrieval
EA-VTR can efficiently encode frame-level and video-level visual representations simultaneously, enabling detailed event content and complex event temporal cross-modal alignment, ultimately enhancing the comprehensive understanding of video events.
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?
Dominant dual-encoder models enable efficient image-text retrieval but suffer from limited accuracy while the cross-encoder models offer higher accuracy at the expense of efficiency.
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis.
An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
In this paper, we question if the \textit{extremely simple} lightweight ViTs' fine-tuning performance can also benefit from this pre-training paradigm, which is considerably less studied yet in contrast to the well-established lightweight architecture design methodology.
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation.
BEV$^2$PR: BEV-Enhanced Visual Place Recognition with Structural Cues
2) The lower layers of the pre-trained backbone from BEV generation are shared for visual and structural streams in VPR, facilitating the learning of fine-grained local features in the visual stream.
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
On the other hand, PromptIQA is trained on a mixed dataset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to effectively adapt to new requirements.
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training
In vision-language pre-training (VLP), masked image modeling (MIM) has recently been introduced for fine-grained cross-modal alignment.
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval
In this work, we propose the UNIFY framework, which learns lexicon representations to capture fine-grained semantics and combines the strengths of latent and lexicon representations for video-text retrieval.
NFT1000: A Cross-Modal Dataset for Non-Fungible Token Retrieval
In this paper, we will introduce a benchmark dataset named "NFT Top1000 Visual-Text Dataset" (NFT1000), containing 7. 56 million image-text pairs, and being collected from 1000 most famous PFP1 NFT collections2 by sales volume on the Ethereum blockchain.
GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment
Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean opinion score for an image.
A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection
Our motivation stems from the observation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format.
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
I2V-Adapter adeptly propagates the unnoised input image to subsequent noised frames through a cross-frame attention mechanism, maintaining the identity of the input image without any changes to the pretrained T2V model.
Set Prediction Guided by Semantic Concepts for Diverse Video Captioning
Each caption in the set is attached to a concept combination indicating the primary semantic content of the caption and facilitating element alignment in set prediction.
ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking
To this end, we non-uniformly resize the cropped image to have a smaller input size while the resolution of the area where the target is more likely to appear is higher and vice versa.
Real-time Monocular Depth Estimation on Embedded Systems
Depth sensing is of paramount importance for unmanned aerial and autonomous vehicles.
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval
ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information.
Ranked #46 on
Visual Reasoning
on Winoground
AUNet: Learning Relations Between Action Units for Face Forgery Detection
Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action Units Relation Learning framework to improve the generality of forgery detection.
Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization
The ISP is less programmable and consists of a series of processing modules.
Order-Prompted Tag Sequence Generation for Video Tagging
This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics.
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning
However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one.
Cross-Architecture Knowledge Distillation
However, most existing knowledge distillation methods only consider homologous-architecture distillation, such as distilling knowledge from CNN to CNN.
PIC 4th Challenge: Semantic-Assisted Multi-Feature Encoding and Multi-Head Decoding for Dense Video Captioning
The task of Dense Video Captioning (DVC) aims to generate captions with timestamps for multiple events in one video.
SiamMask: A Framework for Fast Online Object Tracking and Segmentation
In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method.
PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world.
Ranked #2 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Narrowing the Gap: Improved Detector Training with Noisy Location Annotations
Experiments for both synthesized and real-world scenarios consistently demonstrate the effectiveness of our approach, e. g., our method increases the degraded performance of the FCOS detector from 33. 6% AP to 35. 6% AP on COCO.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
A Closer Look at Self-Supervised Lightweight Vision Transformers
We also point out some defects of such pre-training, e. g., failing to benefit from large-scale pre-training data and showing inferior performance on data-insufficient downstream tasks.
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning.
Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning
They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text.
CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation
In this paper, we propose to CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration benchmark, to facilitate research and application in video titling and video retrieval in Chinese.
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation
Open-vocabulary object detection aims to detect novel object categories beyond the training set.
Ranked #31 on
Open Vocabulary Object Detection
on MSCOCO
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
Learning Target-aware Representation for Visual Tracking via Informative Interactions
The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced by our extensive experiments on multiple benchmarks.
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training.
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
The datasets will be released to facilitate the development of video captioning metrics.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information.
SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction
CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation.
Differentiable Convolution Search for Point Cloud Processing
It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
Learn to Match: Automatic Matching Network Design for Visual Tracking
Siamese tracking has achieved groundbreaking performance in recent years, where the essence is the efficient matching operator cross-correlation and its variants.
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition.
Ranked #13 on
Skeleton Based Action Recognition
on N-UCLA
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer
In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
In this paper, we show the existence of universal perturbations that can enable the targeted attack, e. g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling
However, the most suitable positions for inferring different targets, i. e., the object category and boundaries, are generally different.
One More Check: Making "Fake Background" Be Tracked Again
Eventually, it helps to reload the ``fake background'' and repair the broken tracklets.
A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
Finally, the application prospect of the analytical method in this field is discussed.
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model
Inspired by the findings of our investigation, we propose a novel multi-modal video saliency model consisting of three branches: visual, audio and face.
Weather Analogs with a Machine Learning Similarity Metric for Renewable Resource Forecasting
The central core of the AnEn technique is a similarity metric that sorts historical forecasts with respect to a new target prediction.
Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment
Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.
DSIC: Dynamic Sample-Individualized Connector for Multi-Scale Object Detection
Furthermore, these two components are both plug-and-play and can be embedded in any backbone.
Rethinking the competition between detection and ReID in Multi-Object Tracking
However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
Ranked #1 on
Multi-Object Tracking
on HiEve
(using extra training data)
Towards Accurate Pixel-wise Object Tracking by Attention Retrieval
We first build a look-up-table (LUT) with the ground-truth mask in the starting frame, and then retrieves the LUT to obtain an attention map for spatial constraints.
Ocean: Object-aware Anchor-free Tracking
In this paper, we propose a novel object-aware anchor-free network to address this issue.
Ranked #2 on
Visual Object Tracking
on VOT2019
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy.
Ranked #9 on
Video Captioning
on VATEX
(using extra training data)
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.
Ranked #96 on
Instance Segmentation
on COCO test-dev
Anchor Diffusion for Unsupervised Video Object Segmentation
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow.
Ranked #21 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
VATEX Captioning Challenge 2019: Multi-modal Information Fusion and Multi-stage Training Strategy for Video Captioning
Multi-modal information is essential to describe what has happened in a video.
Probabilistic Forecasting using Deep Generative Models
In order to provide a meaningful probabilistic forecast, the AnEn method requires storing a historical set of past predictions and observations in memory for a period of at least several months and spanning the seasons relevant for the prediction of interest.
Multimodal Semantic Attention Network for Video Captioning
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.
Fast Online Object Tracking and Segmentation: A Unifying Approach
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Ranked #3 on
Visual Object Tracking
on YouTube-VOS 2018
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
Distractor-aware Siamese Networks for Visual Object Tracking
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
Ranked #11 on
Visual Object Tracking
on VOT2017/18
Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification
Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps.
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on
Visual Object Tracking
on OTB-2013
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection From Videos
In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background.
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
Tensor Power Iteration for Multi-Graph Matching
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic.
Local Subspace Collaborative Tracking
To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition
In this paper, a multi-feature max-margin hierarchical Bayesian model (M3HBM) is proposed for action recognition.
Multi-target Tracking with Motion Context in Tensor Power Iteration
In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.
Towards Multi-view and Partially-Occluded Face Alignment
During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks.
Human Action Recognition Based on Context-Dependent Graph Kernels
Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model.
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed.
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.
Multi-target Tracking by Rank-1 Tensor Approximation
In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an 1 norm tensor power iteration solution.
Illumination Estimation Based on Bilayer Sparse Coding
In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously.
3D R Transform on Spatio-temporal Interest Points for Action Recognition
In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points.
