11 citations · 12 across the 12 of their papers we have counts for
5 papers · 2 filters
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…