11 citations · 12 across the 12 of their papers we have counts for
17 papers
Generative Representation Learning for Scalable Latent Tokenization
Alan Baade, Kyle Sargent, Vincent Sitzmann +4
We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by f…
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…
DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution
L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan +2
In the age of advanced large language models (LLMs), the boundaries between human and AI-generated text are becoming increasingly blurred. We address the challenge of segmenting mi…
Key-Conditioned Orthonormal Transform Gating (K-OTG): Multi-Key Access Control with Hidden-State Scrambling for LoRA-Tuned Models
Muhammad Haris Khan
We present a simple, PEFT-compatible mechanism that enforces secret-key access control in instruction-tuned language models. K-OTG trains on a dual-path corpus: authorized examples…
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…
RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods
Eman Ali, Chetan Arora, Muhammad Haris Khan
In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shif…
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…
Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification
Salma Hassan, Hamad Al Hammadi, Ibrahim Mohammed +1
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In…
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…