Search Results for author:
Found 123 papers, 91 papers with code
Fixing Localization Errors to Improve Image Classification
This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes.
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
In the current work, we introduce a consistency-guided bottleneck in an image reconstruction-based pipeline that leverages landmark consistency, a measure of compatibility score with the pseudo-ground truth to generate adaptive heatmaps.
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment
To address this challenge, we propose the Self Structural Semantic Alignment (S^3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning.
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT).
Towards Instance-adaptive Inference for Federated Learning
However, the performance of the global model is often hampered by non-i. i. d.
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.
Foundational Models Defining a New Era in Vision: A Survey and Outlook
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks.
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
To the best of our knowledge, this is the first regularization framework for prompt learning that avoids overfitting by jointly attending to pre-trained model features, the training trajectory during prompting, and the textual diversity.
Ranked #1 on
Prompt Engineering
on ImageNet V2
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Video transformer designs are based on self-attention that can model global context at a high computational cost.
PromptIR: Prompting for All-in-One Blind Image Restoration
We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation.
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention.
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks.
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data.
Ranked #1 on
Video-based Generative Performance Benchmarking (Temporal Understanding)
on VideoInstruct
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+5
Modulate Your Spectrum in Self-Supervised Learning
We show that whitening transformation is a special instance of ST by definition, and there exist other instances that can avoid collapse by our empirical investigation.
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Burst image processing is becoming increasingly popular in recent years.
Remote Sensing Change Detection With Transformers Trained from Scratch
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark.
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting.
Burstormer: Burst Image Restoration and Enhancement Transformer
Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges feature information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions.
Generative Multiplane Neural Radiance for 3D-Aware Image Generation
We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views.
Video Instance Segmentation in an Open-World
Open-world formulation relaxes the close-world static-learning assumption as follows: (a) first, it distinguishes a set of known categories as well as labels an unknown object as `unknown' and then (b) it incrementally learns the class of an unknown as and when the corresponding semantic labels become available.
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed.
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Since the original formulation of our loss depends on the counts of true positives and false positives in a minibatch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions.
Boosting Adversarial Transferability using Dynamic Cues
Our temporal prompts are the result of a learnable transformation that allows optimizing for temporal gradients during an adversarial attack to fool the motion dynamics.
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Recently, different adversarial training defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well studied adversarial attacks such as PGD.
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes.
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks.
Fine-tuned CLIP Models are Efficient Video Learners
Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain.
Person Image Synthesis via Denoising Diffusion Model
In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution.
An Investigation into Whitening Loss for Self-supervised Learning
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse.
CLIP model is an Efficient Continual Learner
In this work, we show that a frozen CLIP (Contrastive Language-Image Pretraining) model offers astounding continual learning performance without any fine-tuning (zero-shot evaluation).
MaPLe: Multi-modal Prompt Learning
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks.
Ranked #1 on
Prompt Engineering
on FGVC-Aircraft
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Neural networks have proven to be very powerful at computer vision tasks.
Situation Awareness for Automated Surgical Check-listing in AI-Assisted Operating Room
Nowadays, there are more surgical procedures that are being performed using minimally invasive surgery (MIS).
Transformers in Remote Sensing: A Survey
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade.
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects.
3D Vision with Transformers: A Survey
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field.
D3Former: Debiased Dual Distilled Transformer for Incremental Learning
In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks.
Self-Distilled Vision Transformer for Domain Generalization
Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains.
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Our training approach is based on a min-max scheme which reduces overfitting via an adversarial objective and thus optimizes for a more generalizable surrogate model.
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision.
Ranked #1 on
Open Vocabulary Object Detection
on OpenImages-v4
Open Vocabulary Attribute Detection
Zero-Shot Object Detection
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data.
Ranked #1 on
Open-World Semi-Supervised Learning
on CIFAR-10
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Our EdgeNeXt model with 1. 3M parameters achieves 71. 2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2. 2% with 28% reduction in FLOPs.
Ranked #29 on
Semantic Segmentation
on PASCAL VOC 2012 test
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging
We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation.
Learning Enriched Features for Fast Image Restoration and Enhancement
In the former case, spatial details are preserved but the contextual information cannot be precisely encoded.
Energy-based Latent Aligner for Incremental Learning
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks.
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
When using the ResNet50 backbone, our MS-STS achieves a mask AP of 50. 1 %, outperforming the best reported results in literature by 2. 7 % and by 4. 8 % at higher overlap threshold of AP_75, while being comparable in model size and speed on Youtube-VIS 2019 val.
Transformers in Medical Imaging: A Survey
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing
In an autonomous driving system, perception - identification of features and objects from the environment - is crucial.
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff.
Spatio-temporal Relation Modeling for Few-shot Action Recognition
Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101.
Ranked #1 on
Few Shot Action Recognition
on HMDB51
DoodleFormer: Creative Sketch Drawing with Transformers
Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects.
OW-DETR: Open-world Detection Transformer
In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC.
Self-supervised Video Transformer
To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT).
Ranked #52 on
Action Recognition
on UCF101
Class-agnostic Object Detection with Multi-modal Transformer
This has been a long-standing question in computer vision.
Ranked #1 on
Class-agnostic Object Detection
on COCO
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Ranked #1 on
Grayscale Image Denoising
on Urban100 sigma15
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction.
Burst Image Restoration and Enhancement
Our central idea is to create a set of pseudo-burst features that combine complementary information from all the input burst frames to seamlessly exchange information.
Ranked #2 on
Burst Image Super-Resolution
on BurstSR
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
Discriminative Region-based Multi-Label Zero-Shot Learning
We note that the best existing multi-label ZSL method takes a shared approach towards attending to region features with a common set of attention maps for all the classes.
Ranked #2 on
Multi-label zero-shot learning
on Open Images V4
Learning Discriminative Representations for Multi-Label Image Recognition
Multi-label recognition is a fundamental, and yet is a challenging task in computer vision.
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Identifying potential threats concealed within the baggage is of prime concern for the security staff.
On Improving Adversarial Transferability of Vision Transformers
(ii) Token Refinement: We then propose to refine the tokens to further enhance the discriminative capacity at each block of ViT.
Intriguing Properties of Vision Transformers
We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e. g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content.
Rich Semantics Improve Few-shot Learning
Human learning benefits from multi-modal inputs that often appear as rich semantics (e. g., description of an object's attributes while learning about it).
Ranked #1 on
Few-Shot Image Classification
on Oxford 102 Flower
(using extra training data)
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection
We also contribute fresh temporal complex activity annotation for the recently released ROAD autonomous driving and SARAS-ESAD surgical action datasets and show the adaptability of our framework to different domains.
Handwriting Transformers
We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns.
On Generating Transferable Targeted Perturbations
To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains.
Orthogonal Projection Loss
The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes.
Towards Open World Object Detection
Humans have a natural instinct to identify unknown object instances in their environments.
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
Equivariance or invariance has been employed standalone in the previous works; however, to the best of our knowledge, they have not been used jointly.
ROAD: The ROad event Awareness Dataset for Autonomous Driving
We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving.
Robust normalizing flows using Bernstein-type polynomials
Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e. g., instrumentation errors, or added random noise.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #4 on
Deblurring
on RSBlur
Generative Multi-Label Zero-Shot Learning
Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge.
Ranked #8 on
Multi-label zero-shot learning
on NUS-WIDE
Transformers in Vision: A Survey
Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems.
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds.
Meta-learning the Learning Trends Shared Across Tasks
This demonstrates their ability to acquire transferable knowledge, a capability that is central to human learning.
Synthesizing the Unseen for Zero-shot Object Detection
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on
Zero-Shot Object Detection
on ImageNet Detection
Generalized Zero-Shot Object Detection
Zero-Shot Object Detection
Task-Driven Learning of Contour Integration Responses in a V1 Model
Under difficult viewing conditions, the brain's visual system uses a variety of modulatory techniques to augment its core feed-forward signals.
Conditional Generative Modeling via Learning the Latent Space
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings.
Attention Guided Semantic Relationship Parsing for Visual Question Answering
Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question Answering (VQA).
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
Detecting baggage threats is one of the most difficult tasks, even for expert officers.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Stylized Adversarial Defense
In contrast to existing adversarial training methods that only use class-boundary information (e. g., using a cross-entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model.
Self-supervised Knowledge Distillation for Few-shot Learning
Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process.
Ranked #12 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
A Self-supervised Approach for Adversarial Robustness
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e. g., for classification, segmentation and object detection.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
In the last two decades, baggage scanning has globally become one of the prime aviation security concerns.
Semi-supervised Learning for Few-shot Image-to-Image Translation
In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training.
iTAML: An Incremental Task-Agnostic Meta-learning Approach
In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks.
Incremental Object Detection via Meta-Learning
In a real-world setting, object instances from new classes can be continuously encountered by object detectors.
CycleISP: Real Image Restoration via Improved Data Synthesis
This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
Ranked #9 on
Image Denoising
on DND
(using extra training data)
Any-Shot Object Detection
Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background.
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
Ranked #4 on
Image Denoising
on DND
Durocmien: A deep framework for duroc skeleton extraction in constraint environment
Farm animal behavior analysis is a crucial tasks for the industrial farming.
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other.
Ranked #14 on
Fine-Grained Image Classification
on Stanford Dogs
Towards Partial Supervision for Generic Object Counting in Natural Scenes
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
Image Classification
Image-level Supervised Instance Segmentation
+2
Spectral-GANs for High-Resolution 3D Point-cloud Generation
Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners.
Representation Learning on Unit Ball with 3D Roto-Translational Equivariance
In this work, we propose a novel `\emph{volumetric convolution}' operation that can effectively model and convolve arbitrary functions in $\mathbb{B}^3$.
AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
Several studies show that animal needs are often expressed through their faces.
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Existing networks directly learn feature representations on 3D point clouds for shape analysis.
Understanding More about Human and Machine Attention in Deep Neural Networks
With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms.
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
With this insight, we design a novel Dispersion-based Clustering (DBC) approach which can discover the underlying patterns in data.
Ranked #15 on
Unsupervised Person Re-Identification
on Market-1501
An Adaptive Random Path Selection Approach for Incremental Learning
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage.
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances.
Ranked #1 on
Object Detection
on iSAID
A Deep Journey into Super-resolution: A survey
Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications.
Learning Digital Camera Pipeline for Extreme Low-Light Imaging
In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR).
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #5 on
Adversarial Defense
on CIFAR-10
Max-margin Class Imbalanced Learning with Gaussian Affinity
Real-world object classes appear in imbalanced ratios.
Striking the Right Balance with Uncertainty
Rare classes tend to get a concentrated representation in the classification space which hampers the generalization of learned boundaries to new test examples.
Volumetric Convolution: Automatic Representation Learning in Unit Ball
Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks.
Polarity Loss for Zero-shot Object Detection
This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes.
Ranked #4 on
Zero-Shot Object Detection
on PASCAL VOC'07
A Context-aware Capsule Network for Multi-label Classification
Recently proposed Capsule Network is a brain inspired architecture that brings a new paradigm to deep learning by modelling input domain variations through vector based representations.
Visual Affordance and Function Understanding: A Survey
Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries.
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
Deep Multiple Instance Learning for Zero-shot Image Tagging
In-line with the success of deep learning on traditional recognition problem, several end-to-end deep models for zero-shot recognition have been proposed in the literature.
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the `recognition' and `localization' of an unseen category.
Regularization of Deep Neural Networks with Spectral Dropout
We cast the proposed approach in the form of regular Convolutional Neural Network (CNN) weight layers using a decorrelation transform with fixed basis functions.
Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
Person re-identification aims at establishing the identity of a pedestrian from a gallery that contains images of multiple people obtained from a multi-camera system.
