Search Results for author:
Found 80 papers, 21 papers with code
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework.
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns.
Stock Market Prediction via Deep Learning Techniques: A Survey
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme.
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
The key insight of this design is that the cross-attention mechanism in the transformer could be an ideal solution to align the discriminative texture clues from the original image with the canonical view image, which could compensate for the low-quality texture information of the canonical view image.
Domain Generalization
Generalizable Person Re-identification
+1
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones.
Regularizing Neural Network Training via Identity-wise Discriminative Feature Suppression
It suppresses features that can be utilized to identify individual instances among samples within each class.
Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction
Existing works usually formulate the span detection as a 1D token tagging problem, and model the sentiment recognition with a 2D tagging matrix of token pairs.
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data.
Don't Stop Learning: Towards Continual Learning for the CLIP Model
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.
Dual Decision Improves Open-Set Panoptic Segmentation
Then we distinguish the "unknown things" from the background by using the additional object prediction head.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
Ranked #1 on
Stock Price Prediction
on Astock
Progressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification.
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented forgeries with a pool of forgery configurations and strengthen the "sensitivity" to the forgeries by enforcing the model to predict the forgery configurations.
Multi-Domain Joint Training for Person Re-Identification
Our discovery is that training with such an adaptive model can better benefit from more training samples.
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.
Center Prediction Loss for Re-identification
In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.
CAT: Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison.
Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification
Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under different viewpoints.
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Fashion products typically feature in compositions of a variety of styles at different clothing parts.
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems
Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context.
Ranked #2 on
Task-Oriented Dialogue Systems
on KVRET
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain.
Towards Using Count-level Weak Supervision for Crowd Counting
Most existing crowd counting methods require object location-level annotation, i. e., placing a dot at the center of an object.
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years.
Semi-supervised Learning via Conditional Rotation Angle Estimation
Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.
To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions
Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training.
Improving Distant Supervised Relation Extraction by Dynamic Neural Network
To account for this style shift, the model should adjust its parameters in accordance with entity types.
Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction
Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation.
Structured Binary Neural Networks for Image Recognition
Experiments on both classification, semantic segmentation and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature.
In defense of OSVOS
Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i. e. online fine-tuning on the target instance.
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
Furthermore, we propose a second progressive quantization scheme which gradually decreases the bit-width from high-precision to low-precision during training.
V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices
One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.
Training Quantized Neural Networks with a Full-precision Auxiliary Module
In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function.
A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning
A sketch based 3D shape retrieval
RPC: A Large-Scale Retail Product Checkout Dataset
The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products.
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.
Mask-aware networks for crowd counting
Our rationale is that the mask prediction could be better modeled as a binary segmentation problem and the difficulty of estimating the density could be reduced if the mask is known.
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources.
Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal
Reflections often obstruct the desired scene when taking photos through glass panels.
Towards Effective Deep Embedding for Zero-Shot Learning
Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier.
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.
Towards Effective Low-bitwidth Convolutional Neural Networks
This paper tackles the problem of training a deep convolutional neural network with both low-precision weights and low-bitwidth activations.
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity.
Towards Context-Aware Interaction Recognition for Visual Relationship Detection
The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent.
Visually Aligned Word Embeddings for Improving Zero-shot Learning
To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE).
Multi-Attention Network for One Shot Learning
One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image.
Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity.
Ranked #14 on
Pose Estimation
on MPII Human Pose
Towards Context-aware Interaction Recognition
Recognizing how objects interact with each other is a crucial task in visual recognition.
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks.
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition.
Sequential Person Recognition in Photo Albums with a Recurrent Network
In this work, we propose to model the relational information between people as a sequence prediction task.
Graph-Structured Representations for Visual Question Answering
This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions.
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification.
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
Instance retrieval requires one to search for images that contain a particular object within a large corpus.
What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution
The key observation motivating our approach is that "regular object" images, "unusual object" images and "other objects" images exhibit different region-level scores in terms of both the score values and the spatial distributions.
Less is more: zero-shot learning from online textual documents with noise suppression
Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes.
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution
To address this problem, we propose a novel approach by inspecting the distribution of the detection scores at multiple image regions based on the detector trained from the "regular object" and "other objects".
Order-aware Convolutional Pooling for Video Based Action Recognition
Most video based action recognition approaches create the video-level representation by temporally pooling the features extracted at each frame.
Compositional Model based Fisher Vector Coding for Image Classification
To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions.
Cross-convolutional-layer Pooling for Image Recognition
Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation.
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN).
Mining Mid-level Visual Patterns with Deep CNN Activations
The purpose of mid-level visual element discovery is to find clusters of image patches that are both representative and discriminative.
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition.
Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition
One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs.
The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification
This paper, however, advocates that if used appropriately convolutional layer activations can be turned into a powerful image representation which enjoys many advantages over fully-connected layer activations.
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC).
Mid-level Deep Pattern Mining
We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used.
A Generalized Probabilistic Framework for Compact Codebook Creation
In the third criterion, which shows the best merging performance, we propose a max-margin-based parameter estimation method and apply it with multinomial distribution.
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network
Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivitybased biomarkers for the Alzheimer's disease (AD).
