298 citations · 371 across the 19 of their papers we have counts for
24 papers
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition
Hisham A. Alyahya, Haidar Khan, Yazeed Alnumay +2
We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses…
SEA-LION: Southeast Asian Languages in One Network
Raymond Ng, Thanh Ngan Nguyen, Yuli Huang +28
Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages. However, the majority…
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study
Menglong Cui, Pengzhi Gao, Wei Liu +2
Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Context Diffusion: In-Context Aware Image Generation
Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey +3
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context. Recent work tackles such in-conte…
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
COLA: A Benchmark for Compositional Text-to-image Retrieval
Arijit Ray, Filip Radenovic, Abhimanyu Dubey +3
Compositional reasoning is a hallmark of human visual intelligence. Yet, despite the size of large vision-language models, they struggle to represent simple compositions by combini…
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian +6
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve th…
Neural Basis Models for Interpretability
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically b…
Scalable Interpretability via Polynomials
Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan
Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they…
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals
Simon Vandenhende, Dhruv Mahajan, Filip Radenovic +1
A visual counterfactual explanation replaces image regions in a query image with regions from a distractor image such that the system's decision on the transformed image changes to…
The 2021 Image Similarity Dataset and Challenge
Matthijs Douze, Giorgos Tolias, Ed Pizzi +9
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to…
Large-Scale Attribute-Object Compositions
Filip Radenovic, Animesh Sinha, Albert Gordo +2
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best…
Attention-Based Query Expansion Learning
Albert Gordo, Filip Radenovic, Tamara Berg
Query expansion is a technique widely used in image search consisting in combining highly ranked images from an original query into an expanded query that is then reissued, general…
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower
Giorgos Tolias, Filip Radenovic, Ondřej Chum
Access to online visual search engines implies sharing of private user content - the query images. We introduce the concept of targeted mismatch attack for deep learning based retr…
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Filip Radenović, Ahmet Iscen, Giorgos Tolias +2
In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset…
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
Dmytro Mishkin, Filip Radenovic, Jiri Matas
A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably…
Fine-tuning CNN Image Retrieval with No Human Annotation
Filip Radenović, Giorgos Tolias, Ondřej Chum
Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representat…
Deep Shape Matching
Filip Radenović, Giorgos Tolias, Ondřej Chum
We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient m…
Working hard to know your neighbor's margins: Local descriptor learning loss
Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic +1
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the dist…
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Martin Cadik, Jan Vasicek, Michal Hradis +2
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand imag…
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Filip Radenović, Giorgos Tolias, Ondřej Chum
Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order…
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
Filip Radenovic, Herve Jegou, Ondrej Chum
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval. In particular, the method of joint dimens…