98 citations · 136 across the 20 of their papers we have counts for
16 papers · 1 filter
CrowdCue: Specialist-Cue Conditioning for Vision-Language Crowd Counting
Moshiur Farazi, Bekir Ciftler, Abdulhalim Dandoush +1
Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene, yet their raw counting accu…
When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study
Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji +3
Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remains underexplored. To address…
The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models
Moshiur Farazi, Sameera Ramasinghe, Bekir Sait Ciftler +2
Auxiliary signal pathways in VLMs are routinely fitted with learnable gates so the optimiser can decide how much of the signal to admit. We find that the optimiser almost always de…
HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning
Moshiur Farazi, Sameera Ramasinghe, Mahbub Ahmed Turza +1
Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to inject explicit scene graph tripl…
Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification
Hanif Rasyidi, Moshiur Farazi
This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challe…
Label Semantics for Robust Hyperspectral Image Classification
Rafin Hassan, Zarin Tasnim Roshni, Rafiqul Bari +4
Hyperspectral imaging (HSI) classification is a critical tool with widespread applications across diverse fields such as agriculture, environmental monitoring, medicine, and materi…
VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection
Yu Cui, Moshiur Farazi
Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related. The…
Recursive Training for Zero-Shot Semantic Segmentation
Ce Wang, Moshiur Farazi, Nick Barnes
General purpose semantic segmentation relies on a backbone CNN network to extract discriminative features that help classify each image pixel into a 'seen' object class (ie., the o…
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM
Zahidul Islam, Mohammad Rukonuzzaman, Raiyan Ahmed +2
Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned securit…
Improving Action Quality Assessment using Weighted Aggregation
Shafkat Farabi, Hasibul Himel, Fakhruddin Gazzali +3
Action quality assessment (AQA) aims at automatically judging human action based on a video of the said action and assigning a performance score to it. The majority of works in the…
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Sameera Ramasinghe, Moshiur Farazi, Salman Khan +2
Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output m…
Attention Guided Semantic Relationship Parsing for Visual Question Answering
Moshiur Farazi, Salman Khan, Nick Barnes
Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question…
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models
Moshiur R. Farazi, Salman H. Khan, Nick Barnes
Visual Question Answering (VQA) has emerged as a Visual Turing Test to validate the reasoning ability of AI agents. The pivot to existing VQA models is the joint embedding that is…
Question-Agnostic Attention for Visual Question Answering
Moshiur R Farazi, Salman H Khan, Nick Barnes
Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question. For this purpose, several m…
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts
Moshiur R Farazi, Salman H Khan, Nick Barnes
Current Visual Question Answering (VQA) systems can answer intelligent questions about `Known' visual content. However, their performance drops significantly when questions about v…
Reciprocal Attention Fusion for Visual Question Answering
Moshiur R Farazi, Salman H Khan
Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate…