Search Results for author:
Found 35 papers, 29 papers with code
Omni-Scale Feature Learning for Person Re-Identification
As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.
Ranked #2 on
Person Re-Identification
on MSMT17-C
Learning Generalisable Omni-Scale Representations for Person Re-Identification
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
Unsupervised Domain Adaptation
Unsupervised Person Re-Identification
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs).
Domain Generalization with MixStyle
Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e. g., photo vs.~sketch images).
Ranked #57 on
Domain Generalization
on PACS
Learning to Prompt for Vision-Language Models
Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks.
Ranked #2 on
Few-shot Age Estimation
on MORPH Album2
Conditional Prompt Learning for Vision-Language Models
With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets.
Ranked #3 on
Prompt Engineering
on ImageNet V2
Domain Adaptive Ensemble Learning
Each such classifier is an expert to its own domain and a non-expert to others.
Domain Generalization: A Survey
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce.
Semi-Supervised Domain Generalization with Stochastic StyleMatch
We find that the DG methods, which by design are unable to handle unlabeled data, perform poorly with limited labels in SSDG; the SSL methods, especially FixMatch, obtain much better results but are still far away from the basic vanilla model trained using full labels.
MixStyle Neural Networks for Domain Generalization and Adaptation
MixStyle is easy to implement with a few lines of code, does not require modification to training objectives, and can fit a variety of learning paradigms including supervised domain generalization, semi-supervised domain generalization, and unsupervised domain adaptation.
Generalized Out-of-Distribution Detection: A Survey
In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i. e., AD, ND, OSR, OOD detection, and OD.
Full-Spectrum Out-of-Distribution Detection
In this paper, we take into account both shift types and introduce full-spectrum OOD (FS-OOD) detection, a more realistic problem setting that considers both detecting semantic shift and being tolerant to covariate shift; and designs three benchmarks.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos.
Ranked #7 on
Unsupervised Video Summarization
on TvSum
Panoptic Scene Graph Generation
Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i. e., objects are detected using bounding boxes followed by prediction of their pairwise relationships.
Ranked #5 on
Panoptic Scene Graph Generation
on PSG Dataset
On-Device Domain Generalization
Another interesting observation is that the teacher-student gap on out-of-distribution data is bigger than that on in-distribution data, which highlights the capacity mismatch issue as well as the shortcoming of KD.
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning.
Neural Prompt Search
The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer.
Ranked #1 on
Image Classification
on OmniBenchmark
(using extra training data)
Open-Vocabulary DETR with Conditional Matching
To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image.
Ranked #21 on
Open Vocabulary Object Detection
on MSCOCO
Contextual Object Detection with Multimodal Large Language Models
Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction.
What Makes Good Examples for Visual In-Context Learning?
To overcome the problem, we propose a prompt retrieval framework to automate the selection of in-context examples.
Unified Vision and Language Prompt Learning
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision community since the emergence of large vision-language models like CLIP.
Panoptic Video Scene Graph Generation
PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
Learning to Generate Novel Domains for Domain Generalization
This explicitly increases the diversity of available training domains and leads to a more generalizable model.
Ranked #65 on
Domain Generalization
on PACS
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings.
Domain Attention Consistency for Multi-Source Domain Adaptation
Importantly, the attention module is supervised by a consistency loss, which is imposed on the distributions of channel attention weights between source and target domains.
Detecting Humans in RGB-D Data with CNNs
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs.
Dynamic Instance Domain Adaptation
In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain.
Video Summarisation by Classification with Deep Reinforcement Learning
Most existing video summarisation methods are based on either supervised or unsupervised learning.
Deep Domain-Adversarial Image Generation for Domain Generalisation
This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier.
Ranked #63 on
Domain Generalization
on PACS
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems.
Learning to Augment via Implicit Differentiation for Domain Generalization
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains.
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch
This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature.
Open-Vocabulary Calibration for Vision-Language Models
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few.
