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Found 58 papers, 21 papers with code
Hierarchical Graph Pattern Understanding for Zero-Shot VOS
However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene.
BatchNorm-based Weakly Supervised Video Anomaly Detection
In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection
Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training.
Ranked #5 on
Anomaly Detection
on MVTec AD
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects.
Attention Map Guided Transformer Pruning for Edge Device
Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity.
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder
Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query.
Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-identification
Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Then we utilize the fused prototype to guide the final segmentation of the query image.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
Dual ResGCN for Balanced Scene GraphGeneration
We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue.
Auto-Encoding Twin-Bottleneck Hashing
One bottleneck (i. e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i. e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes.
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.
Cooperative Cross-Stream Network for Discriminative Action Representation
It extracts this complementary information of different modality from a connection block, which aims at exploring correlations of different stream features.
Ranked #15 on
Action Recognition
on HMDB-51
(using extra training data)
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning
Since deep networks are capable of memorizing the entire dataset, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks.
Temporal Reasoning Graph for Activity Recognition
In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales.
Ranked #53 on
Action Recognition
on Something-Something V1
Make a Face: Towards Arbitrary High Fidelity Face Manipulation
Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity.
Extracting Visual Knowledge from the Internet: Making Sense of Image Data
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.
Dynamically Visual Disambiguation of Keyword-based Image Search
To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables
Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables.
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition.
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision.
TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights
Despite the remarkable success of Convolutional Neural Networks (CNNs) on generalized visual tasks, high computational and memory costs restrict their comprehensive applications on consumer electronics (e. g., portable or smart wearable devices).
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval
The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.
Zero-Shot Sketch-Image Hashing
As an important part of ZSIH, we formulate a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
Neural Stereoscopic Image Style Transfer
While each view of the stereoscopic pair is processed in an individual path, a novel feature aggregation strategy is proposed to effectively share information between the two paths.
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
We propose a novel image classification method based on learning hierarchical inter-class structures.
Towards Automatic Construction of Diverse, High-quality Image Dataset
To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.
Discretely Coding Semantic Rank Orders for Supervised Image Hashing
Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data.
Binary Coding for Partial Action Analysis With Limited Observation Ratios
Extensive experiments on four realistic action datasets in terms of three tasks (i. e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.
Zero-Shot Action Recognition With Error-Correcting Output Codes
Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem.
Ranked #4 on
Zero-Shot Action Recognition
on Olympics
Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning
By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces.
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage.
Refining Image Categorization by Exploiting Web Images and General Corpus
To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images.
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision.
Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering
Along with the prosperity of recurrent neural network in modelling sequential data and the power of attention mechanism in automatically identify salient information, image captioning, a. k. a., image description, has been remarkably advanced in recent years.
Binary Subspace Coding for Query-by-Image Video Retrieval
The first approach, termed Inner-product Binary Coding (IBC), preserves the inner relationships of images and videos in a common Hamming space.
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.
Zero-Shot Hashing via Transferring Supervised Knowledge
Supervised knowledge e. g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions.
Bidirectional Long-Short Term Memory for Video Description
Video captioning has been attracting broad research attention in multimedia community.
Learning Binary Codes and Binary Weights for Efficient Classification
The optimization alternatively proceeds over the binary classifiers and image hash codes.
Learning Binary Codes for Maximum Inner Product Search
Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting.
Hashing on Nonlinear Manifolds
In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
Face Image Classification by Pooling Raw Features
We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab code), which is mainly inspired by spatial pyramid pooling in generic image classification.
Face Identification with Second-Order Pooling
Spatial pyramid pooling of features encoded by an over-complete dictionary has been the key component of many state-of-the-art image classification systems.
Generic Image Classification Approaches Excel on Face Recognition
The main finding of this work is that the standard image classification pipeline, which consists of dictionary learning, feature encoding, spatial pyramid pooling and linear classification, outperforms all state-of-the-art face recognition methods on the tested benchmark datasets (we have tested on AR, Extended Yale B, the challenging FERET, and LFW-a datasets).
Fast Approximate L_infty Minimization: Speeding Up Robust Regression
Minimization of the $L_\infty$ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.
