Search Results for author:
Found 46 papers, 14 papers with code
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts.
OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection
Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image.
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
Therefore, in this paper, we utilize the more challenging image-level annotations and propose an adaptive frequency-aware network (AFANet) for weakly-supervised few-shot semantic segmentation (WFSS).
Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception
We propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption.
Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection
Furthermore, a Modality-aware Dynamic Aggregation Module in the modality-complementary flow dynamically aggregates saliency-related cues from both modality-specific flows.
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
In this article, we present a synergy-aware framework, i. e., EventCrab, that adeptly integrates the "lighter" frame-specific networks for dense event frames with the "heavier" point-specific networks for sparse event points, balancing accuracy and efficiency.
Fast Disentangled Slim Tensor Learning for Multi-view Clustering
To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view.
Improving Multi-modal Large Language Model through Boosting Vision Capabilities
QLadder employs a learnable ``\textit{ladder}'' structure to deeply aggregates the intermediate representations from the frozen pretrained visual encoder (e. g., CLIP image encoder).
Ranked #163 on
Visual Question Answering
on MM-Vet
Why pre-training is beneficial for downstream classification tasks?
Interestingly, we discover that only a small amount of pre-trained model's knowledge is preserved for the inference of downstream tasks.
CSGO: Content-Style Composition in Text-to-Image Generation
Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale style transfer dataset containing 210k image triplets, available for the community to explore and research.
IMAGDressing-v1: Customizable Virtual Dressing
Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping experience.
A Recover-then-Discriminate Framework for Robust Anomaly Detection
Specifically, by Case-1, we found that the main reasons detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has-not/has been recovered into its original state.
Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter
The remarkable advancement in text-to-image generation models significantly boosts the research in ID customization generation.
DVF: Advancing Robust and Accurate Fine-Grained Image Retrieval with Retrieval Guidelines
This paper presents a meticulous analysis leading to the proposal of practical guidelines to identify subcategory-specific discrepancies and generate discriminative features to design effective FGIR models.
VRP-SAM: SAM with Visual Reference Prompt
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model.
Spatial Structure Constraints for Weakly Supervised Semantic Segmentation
In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion.
Context Disentangling and Prototype Inheriting for Robust Visual Grounding
In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes.
What Large Language Models Bring to Text-rich VQA?
This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets.
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples
In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-FGVC) plays a vital role in distinguishing intricate subcategories within broader categories.
Delving into Multimodal Prompting for Fine-grained Visual Classification
In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model.
Ranked #10 on
Fine-Grained Image Classification
on CUB-200-2011
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection
An arbitrary setting can be used in DiffusionVMR during inference without consistency with the training phase.
Ranked #4 on
Video Grounding
on QVHighlights
M$^3$Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition
Due to the scarcity of manually annotated data required for fine-grained video understanding, few-shot fine-grained (FS-FG) action recognition has gained significant attention, with the aim of classifying novel fine-grained action categories with only a few labeled instances.
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
Exploring Effective Factors for Improving Visual In-Context Learning
By doing this, the model can leverage the diverse knowledge stored in different parts of the model to improve its performance on new tasks.
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection
However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged.
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection
In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection.
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation
Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples.
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects.
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}.
Ranked #15 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
sub-region localized hashing for fine-grained image retrieval
Specifically, to localize diverse local regions, a sub-region localization module is developed to learn discriminative local features by locating the peaks of non-overlap sub-regions in the feature map.
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps (CAM) to generate pseudo masks as ground-truth.
Weakly-Supervised Object Localization
Weakly supervised Semantic Segmentation
+1
CTNet: Context-based Tandem Network for Semantic Segmentation
Specifically, the Spatial Contextual Module (SCM) is leveraged to uncover the spatial contextual dependency between pixels by exploring the correlation between pixels and categories.
Ranked #75 on
Semantic Segmentation
on ADE20K val
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.
Data-driven Meta-set Based Fine-Grained Visual Classification
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
Face Super-Resolution Guided by 3D Facial Priors
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals
In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval.
Single Image Dehazing via Conditional Generative Adversarial Network
In contrast, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear image is estimated by an end-to-end trainable neural network.
Learning Dual Convolutional Neural Networks for Low-Level Vision
These problems usually involve the estimation of two components of the target signals: structures and details.
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph.
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance.
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification
More specifically, we utilize three Convolutional Neural Networks (CNNs) operating on appearance, motion and audio signals to extract their corresponding features.
Personalized Age Progression with Bi-level Aging Dictionary Learning
Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.
Ranked #2 on
Human Interaction Recognition
on BIT
Weakly-Supervised Dual Clustering for Image Semantic Segmentation
In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i. e., collaboratively performing image segmentation and tag alignment with those regions.
