33 citations · 133 across the 57 of their papers we have counts for
10 papers · 1 filter
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Shehan Munasinghe, Rusiru Thushara, Muhammad Maaz +4
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to vide…
GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Kartik Kuckreja, Muhammad Sohail Danish, Muzammal Naseer +3
Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However,…
Cal-DETR: Calibrated Detection Transformer
Muhammad Akhtar Munir, Salman Khan, Muhammad Haris Khan +2
Albeit revealing impressive predictive performance for several computer vision tasks, deep neural networks (DNNs) are prone to making overconfident predictions. This limits the ado…
Enhancing Novel Object Detection via Cooperative Foundational Models
Rohit Bharadwaj, Muzammal Naseer, Salman Khan +1
In this work, we address the challenging and emergent problem of novel object detection (NOD), focusing on the accurate detection of both known and novel object categories during i…
GLaMM: Pixel Grounding Large Multimodal Model
Hanoona Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly +7
Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recen…
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization
Jameel Hassan, Hanan Gani, Noor Hussein +4
The promising zero-shot generalization of vision-language models such as CLIP has led to their adoption using prompt learning for numerous downstream tasks. Previous works have sho…
Videoprompter: an ensemble of foundational models for zero-shot video understanding
Adeel Yousaf, Muzammal Naseer, Salman Khan +2
Vision-language models (VLMs) classify the query video by calculating a similarity score between the visual features and text-based class label representations. Recently, large lan…
Sentence-level Prompts Benefit Composed Image Retrieval
Yang Bai, Xinxing Xu, Yong Liu +5
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adop…
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
Mamona Awan, Muhammad Haris Khan, Sanoojan Baliah +4
We study a challenging problem of unsupervised discovery of object landmarks. Many recent methods rely on bottlenecks to generate 2D Gaussian heatmaps however, these are limited in…
Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
Shuo Ye, Shiming Chen, Ruxin Wang +5
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categori…