53 citations · 138 across the 22 of their papers we have counts for
24 papers
Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Stephen Gould +1
Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the highest-ranked detector will tr…
The Quest for Winning Tickets in Low-Rank Adapters
Hamed Damirchi, Cristian Rodriguez-Opazo, Ehsan Abbasnejad +2
The Lottery Ticket Hypothesis (LTH) suggests that over-parameterized neural networks contain sparse subnetworks ("winning tickets") capable of matching full model performance when…
An empirical study of the effect of video encoders on Temporal Video Grounding
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Edison Marrese-Taylor +1
Temporal video grounding is a fundamental task in computer vision, aiming to localize a natural language query in a long, untrimmed video. It has a key role in the scientific commu…
Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection
I. M. De la Jara, C. Rodriguez-Opazo, D. Teney +2
Out-of-distribution (OOD) detection is essential for reliably deploying machine learning models in the wild. Yet, most methods treat large pre-trained models as monolithic encoders…
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
Paul Albert, Frederic Z. Zhang, Hemanth Saratchandran +3
Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while…
Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction
Zheyuan Liu, Junyan Wang, Zicheng Duan +2
Text-video prediction (TVP) is a downstream video generation task that requires a model to produce subsequent video frames given a series of initial video frames and text describin…
Temporally Grounding Instructional Diagrams in Unconstrained Videos
Jiahao Zhang, Frederic Z. Zhang, Cristian Rodriguez +3
We study the challenging problem of simultaneously localizing a sequence of queries in the form of instructional diagrams in a video. This requires understanding not only the indiv…
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling
Frederic Z. Zhang, Paul Albert, Cristian Rodriguez-Opazo +2
Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vect…
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Damien Teney +3
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers (ViTs) to convolut…
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Ehsan Abbasnejad +4
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various neural architectures, spanning Transformer-based models li…
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad +4
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box…
MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration
Yifu Wang, Yonhon Ng, Inkyu Sa +4
We present a novel optimization-based Visual-Inertial SLAM system designed for multiple partially overlapped camera systems, named MAVIS. Our framework fully exploits the benefits…
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
Jiahao Zhang, Anoop Cherian, Yanbin Liu +3
Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is betw…
LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando +2
We propose LocFormer, a Transformer-based model for video grounding which operates at a constant memory footprint regardless of the video length, i.e. number of frames. LocFormer i…
Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney +1
We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been…
A Recurrent Vision-and-Language BERT for Navigation
Yicong Hong, Qi Wu, Yuankai Qi +2
Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language(V&L) BERT. However, its application for the task of vision-and-langua…
DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando +2
This paper studies the task of temporal moment localization in a long untrimmed video using natural language query. Given a query sentence, the goal is to determine the start and e…
Language and Visual Entity Relationship Graph for Agent Navigation
Yicong Hong, Cristian Rodriguez-Opazo, Yuankai Qi +2
Vision-and-Language Navigation (VLN) requires an agent to navigate in a real-world environment following natural language instructions. From both the textual and visual perspective…
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
Yizhak Ben-Shabat, Xin Yu, Fatemeh Sadat Saleh +4
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks. In the context of understanding human ac…
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Edison Marrese-Taylor, Cristian Rodriguez-Opazo, Jorge A. Balazs +2
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal…
Sub-Instruction Aware Vision-and-Language Navigation
Yicong Hong, Cristian Rodriguez-Opazo, Qi Wu +1
Vision-and-language navigation requires an agent to navigate through a real 3D environment following natural language instructions. Despite significant advances, few previous works…
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
Dongxu Li, Cristian Rodriguez Opazo, Xin Yu +1
Vision-based sign language recognition aims at helping deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words.…
Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Fatemeh Sadat Saleh +2
This paper studies the problem of temporal moment localization in a long untrimmed video using natural language as the query. Given an untrimmed video and a sentence as the query,…
Action Anticipation By Predicting Future Dynamic Images
Cristian Rodriguez, Basura Fernando, Hongdong Li
Human action-anticipation methods predict what is the future action by observing only a few portion of an action in progress. This is critical for applications where computers have…