Search Results for author:
Found 49 papers, 30 papers with code
Image Manipulation Detection by Multi-View Multi-Scale Supervision
The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images.
Ranked #4 on
Image Manipulation Localization
on COVERAGE
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
As both clues are meant to be semantic-agnostic, the learned features are thus generalizable.
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods.
Dual Encoding for Zero-Example Video Retrieval
This paper attacks the challenging problem of zero-example video retrieval.
Dual Encoding for Video Retrieval by Text
In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own.
Ranked #3 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Mining Dual Emotion for Fake News Detection
Emotion plays an important role in detecting fake news online.
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
Deepfake Network Architecture Attribution
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images.
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch.
Ranked #2 on
Image Registration
on FIRE
Partially Relevant Video Retrieval
To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR).
Ranked #1 on
Partially Relevant Video Retrieval
on TVR
3D Object Detection for Autonomous Driving: A Survey
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes.
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval.
Ranked #1 on
Ad-hoc video search
on TRECVID-AVS20 (V3C1)
(using extra training data)
Fluency-Guided Cross-Lingual Image Captioning
The framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language.
BADet: Boundary-Aware 3D Object Detection from Point Clouds
Specifically, instead of refining each proposal independently as previous works do, we represent each proposal as a node for graph construction within a given cut-off threshold, associating proposals in the form of local neighborhood graph, with boundary correlations of an object being explicitly exploited.
Feature Re-Learning with Data Augmentation for Video Relevance Prediction
Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval.
Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval
In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval.
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks.
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims
By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns.
Learn to Segment Retinal Lesions and Beyond
This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading.
Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams.
Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization
Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams.
Geometrized Transformer for Self-Supervised Homography Estimation
Such a homography allows us to compute cross-attention in a focused manner, where key/value sets required by Transformers can be reduced to small fix-sized regions rather than an entire image.
Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
To quantify the current progress, we propose a simple text2image method, representing a novel test query by a set of images selected from large-scale query log.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.
SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries
Inspired by the initial success of previously few works in combining multiple sentence encoders, this paper takes a step forward by developing a new and general method for effectively exploiting diverse sentence encoders.
Ranked #2 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Learn to Understand Negation in Video Retrieval
We propose a learning based method for training a negation-aware video retrieval model.
Unsupervised Domain Expansion for Visual Categorization
In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.
Ranked #1 on
Unsupervised Domain Expansion
on UDE-DomainNet
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Such sorts of samples are typically in minority in their host domain, so they tend to be overlooked by the domain-specific model, but can be better handled by a model from the other domain.
Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images.
Deep Text Classification Can be Fooled
In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack.
Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction
This paper strives to find the sentence best describing the content of an image or video.
Improving Image Captioning by Concept-based Sentence Reranking
This paper describes our winning entry in the ImageCLEF 2015 image sentence generation task.
Detecting Violence in Video using Subclasses
We enrich the MediaEval 2015 violence dataset by \emph{manually} labeling violence videos with respect to the subclasses.
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training.
Tag Relevance Fusion for Social Image Retrieval
Due to the subjective nature of social tagging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increasing amounts of social-networked images.
Adaptive Tag Selection for Image Annotation
The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth.
Imagination Based Sample Construction for Zero-Shot Learning
Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL.
Hierarchical Attention Networks for Medical Image Segmentation
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
iCap: Interactive Image Captioning with Predictive Text
In this paper we study a brand new topic of interactive image captioning with human in the loop.
Towards Annotation-Free Evaluation of Cross-Lingual Image Captioning
For the first scenario with the references available, we propose two metrics, i. e., WMDRel and CLinRel.
Learning to Disentangle GAN Fingerprint for Fake Image Attribution
Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution.
Multi-Modal Multi-Instance Learning for Retinal Disease Recognition
We propose in this paper Multi-Modal Multi-Instance Learning (MM-MIL) for selectively fusing CFP and OCT modalities.
Lesion Localization in OCT by Semi-Supervised Object Detection
Over 300 million people worldwide are affected by various retinal diseases.
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team.
TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval
For efficient T2VR, we propose TeachCLIP with multi-grained teaching to let a CLIP4Clip based student network learn from more advanced yet computationally heavy models such as X-CLIP, TS2-Net and X-Pool .
Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition
The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology.
SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection
Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features.
PhD: A Prompted Visual Hallucination Evaluation Dataset
The rapid growth of Large Language Models (LLMs) has driven the development of Large Vision-Language Models (LVLMs).
