1 citations · 1 across the 7 of their papers we have counts for
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Finding Distributed Object-Centric Properties in Self-Supervised Transformers
Samyak Rawlekar, Amitabh Swain, Yujun Cai +3
Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, typically observed in [CLS] token attention maps of the final layer. However, the…
Efficiently Disentangling CLIP for Multi-Object Perception
Samyak Rawlekar, Yujun Cai, Yiwei Wang +2
Vision-language models like CLIP excel at recognizing the single, prominent object in a scene. However, they struggle in complex scenes containing multiple objects. We identify a f…
Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
Samyak Rawlekar, Shubhang Bhatnagar, Narendra Ahuja
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prom…
S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video
Hao Zhang, Fang Li, Samyak Rawlekar +1
Reconstructing dynamic articulated objects from a singular monocular video is challenging, requiring joint estimation of shape, motion, and camera parameters from limited views. Cu…
Improving Multi-label Recognition using Class Co-Occurrence Probabilities
Samyak Rawlekar, Shubhang Bhatnagar, Vishnuvardhan Pogunulu Srinivasulu +1
Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged inf…
Learning Implicit Representation for Reconstructing Articulated Objects
Hao Zhang, Fang Li, Samyak Rawlekar +1
3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employin…