4 citations · 11 across the 11 of their papers we have counts for
12 papers · 1 filter
MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation
Syed Talal Wasim, Hamid Suleman, Olga Zatsarynna +2
We present MixANT, a novel architecture for stochastic long-term dense anticipation of human activities. While recent State Space Models (SSMs) like Mamba have shown promise throug…
Analysing the Robustness of Vision-Language-Models to Common Corruptions
Muhammad Usama, Syeda Aishah Asim, Syed Bilal Ali +2
Vision-language models (VLMs) have demonstrated impressive capabilities in understanding and reasoning about visual and textual content. However, their robustness to common image c…
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
Divya Velayudhan, Abdelfatah Ahmed, Mohamad Alansari +10
Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However, current datasets are limited i…
Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models
Jinhui Yi, Syed Talal Wasim, Yanan Luo +2
We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current vi…
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos
Hamid Suleman, Syed Talal Wasim, Muzammal Naseer +1
State-space models (SSMs), exemplified by S4, have introduced a novel context modeling method by integrating state-space techniques into deep learning. However, they struggle with…
GroupMamba: Efficient Group-Based Visual State Space Model
Abdelrahman Shaker, Syed Talal Wasim, Salman Khan +2
State-space models (SSMs) have recently shown promise in capturing long-range dependencies with subquadratic computational complexity, making them attractive for various applicatio…
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan +3
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due t…
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
Syed Talal Wasim, Muzammal Naseer, Salman Khan +2
Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding me…
Hardware Resilience Properties of Text-Guided Image Classifiers
Syed Talal Wasim, Kabila Haile Soboka, Abdulrahman Mahmoud +3
This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. By utilizing enriched text…
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
Muhammad Uzair Khattak, Syed Talal Wasim, Muzammal Naseer +3
Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. Conventionally trained using the task-speci…
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Syed Talal Wasim, Muhammad Uzair Khattak, Muzammal Naseer +3
Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model glo…
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Syed Talal Wasim, Muzammal Naseer, Salman Khan +2
Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, re…