3 citations · 5 across the 13 of their papers we have counts for
16 papers
Composition, Not Conversation: VLMs Lose the Scene, Not the Thread
L. D. M. S. Sai Teja, Ufaq Khan, N. Siva Gopala Krishna +5
Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchmarks present the complete im…
Advancing Video-Text Pretraining with Multi-View Captions
Fida M. Thoker, Renaud Vandeghen, Karen Sanchez +2
Video-text pretraining has achieved remarkable progress through the scaling of models and datasets, yet the quality of language supervision remains underexplored. Existing web-scal…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
MedCTA: A Benchmark for Clinical Tool Agents
Tajamul Ashraf, Hyewon Jeong, Fida Mohammad Thoker +1
To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acquisition, and integration. Exist…
FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales
Jorge L. Rodriguez, Victor Angulo Morales, Areej Alwahas +6
Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining datasets and fixed sensor con…
TrackMAE: Video Representation Learning via Track Mask and Predict
Renaud Vandeghen, Fida Mohammad Thoker, Marc Van Droogenbroeck +1
Masked video modeling (MVM) has emerged as a simple and scalable self-supervised pretraining paradigm, but only encodes motion information implicitly, limiting the encoding of temp…
SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +4
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by removi…
SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +1
Masked video modeling, such as VideoMAE, is an effective paradigm for video self-supervised learning (SSL). However, they are primarily based on reconstructing pixel-level details…
Structured-Noise Masked Modeling for Video, Audio and Beyond
Aritra Bhowmik, Fida Mohammad Thoker, Carlos Hinojosa +2
Masked modeling has emerged as a robust self-supervised learning framework. However, most methods rely on random masking, which disregards the structural properties of different da…
LocoMotion: Learning Motion-Focused Video-Language Representations
Hazel Doughty, Fida Mohammad Thoker, Cees G. M. Snoek
This paper strives for motion-focused video-language representations. Existing methods to learn video-language representations use spatial-focused data, where identifying the objec…
SIGMA: Sinkhorn-Guided Masked Video Modeling
Mohammadreza Salehi, Michael Dorkenwald, Fida Mohammad Thoker +3
Video-based pretraining offers immense potential for learning strong visual representations on an unprecedented scale. Recently, masked video modeling methods have shown promising…
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
Fida Mohammad Thoker, Hazel Doughty, Cees Snoek
We propose a self-supervised method for learning motion-focused video representations. Existing approaches minimize distances between temporally augmented videos, which maintain hi…
How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad +1
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability. In this paper, we investigate how s…
Skeleton-Contrastive 3D Action Representation Learning
Fida Mohammad Thoker, Hazel Doughty, Cees G. M. Snoek
This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton…
Feature-Supervised Action Modality Transfer
Fida Mohammad Thoker, Cees G. M. Snoek
This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are a…
Cross-modal knowledge distillation for action recognition
Fida Mohammad Thoker, Juergen Gall
In this work, we address the problem how a network for action recognition that has been trained on a modality like RGB videos can be adapted to recognize actions for another modali…