11 citations · 12 across the 12 of their papers we have counts for
12 papers · 1 filter
Real-Time Source-Free Object Detection
Sairam VCR, Varun Gopal, Poornima Jain +2
Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detec…
Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision
Kartik Kuckreja, Parul Gupta, Muhammad Haris Khan +1
Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations…
Towards Calibrating Prompt Tuning of Vision-Language Models
Ashshak Sharifdeen, Fahad Shamshad, Muhammad Akhtar Munir +6
Prompt tuning of large-scale vision-language models such as CLIP enables efficient task adaptation without updating model weights. However, it often leads to poor confidence calibr…
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities t…
Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection
Sairam VCR, Rishabh Lalla, Aveen Dayal +4
Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability…
CountZES: Counting via Zero-Shot Exemplar Discovery and Selection
Muhammad Ibraheem Siddiqui, Muhammad Haris Khan
Zero-shot object counting (ZOC) aims to count instances of unseen categories using only a class name. Existing exemplar-based methods discover candidate exemplars through random im…
microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Sathira Silva, Eman Ali, Chetan Arora +1
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong…
Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score
Eman Ali, Sathira Silva, Chetan Arora +1
Vision-language models (VLMs) like CLIP excel in zero-shot learning by aligning image and text representations through contrastive pretraining. Existing approaches to unsupervised…
Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang +5
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-con…
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Muhammad Haaris Khan, Hadrien Reynaud, Bernhard Kainz
Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational…
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models
Eman Ali, Sathira Silva, Muhammad Haris Khan
Vision-language models (VLMs), e.g., CLIP, have shown remarkable potential in zero-shot image classification. However, adapting these models to new domains remains challenging, esp…
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models
Eman Ali, Muhammad Haris Khan
Recent advances in large-scale vision-language models have achieved impressive performance in various zero-shot image classification tasks. While prior studies have demonstrated si…