Search Results for author:
Found 25 papers, 10 papers with code
AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrieval
The proposed model uses a transformer-based architecture designed to estimate image-to-image similarity by capturing interactions within and across images based on their local descriptors.
A Generative Approach for Wikipedia-Scale Visual Entity Recognition
In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia.
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code
SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene.
Retrieval-Enhanced Contrastive Vision-Text Models
Contrastive image-text models such as CLIP form the building blocks of many state-of-the-art systems.
Ranked #3 on
Fine-Grained Image Recognition
on OVEN
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems.
Ranked #1 on
Image Classification
on WebVision-1000
(using extra training data)
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
REVEAL consists of four key components: the memory, the encoder, the retriever and the generator.
Ranked #9 on
Visual Question Answering (VQA)
on OK-VQA
A Memory Transformer Network for Incremental Learning
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.
Learning with Neighbor Consistency for Noisy Labels
Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models.
Class-Balanced Distillation for Long-Tailed Visual Recognition
An effective and simple approach to long-tailed visual recognition is to learn feature representations and a classifier separately, with instance and class-balanced sampling, respectively.
Ranked #13 on
Long-tail Learning
on iNaturalist 2018
Memory-Efficient Incremental Learning Through Feature Adaptation
We assume that the model is updated incrementally for new classes as new data becomes available sequentially. This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding original training images.
Graph convolutional networks for learning with few clean and many noisy labels
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.
Label Propagation for Deep Semi-supervised Learning
In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network.
Local Orthogonal-Group Testing
This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line.
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth.
Mining on Manifolds: Metric Learning without Labels
Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds.
Unsupervised object discovery for instance recognition
Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion.
Panorama to panorama matching for location recognition
Location recognition is commonly treated as visual instance retrieval on "street view" imagery.
Fast Spectral Ranking for Similarity Search
This makes the Euclidean nearest neighbor search biased for this task.
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
Efficient Large-Scale Similarity Search Using Matrix Factorization
Experiments with standard image search benchmarks, including the Yahoo100M dataset comprising 100 million images, show that our method gives comparable (and sometimes superior) accuracy compared to exhaustive search while requiring only 10% of the vector operations and memory.
Memory vectors for similarity search in high-dimensional spaces
We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory.
A comparison of dense region detectors for image search and fine-grained classification
Our results show that the regular dense detector is outperformed by other methods in most situations, leading us to improve the state of the art in comparable setups on standard retrieval and fined-grain benchmarks.
ConceptVision: A Flexible Scene Classification Framework
We introduce ConceptVision, a method that aims for high accuracy in categorizing large number of scenes, while keeping the model relatively simpler and efficient for scalability.
What is usual in unusual videos? Trajectory snippet histograms for discovering unusualness
Unusual events are important as being possible indicators of undesired consequences.
