10 citations · 44 across the 27 of their papers we have counts for
25 papers · 1 filter
How Much Can We Prune? Exploring the Performance Limits of Visual Token Pruning
Janghoon Cho, Shubhankar Borse, Reza Pourreza +4
Visual token pruning is increasingly used to reduce the memory and inference costs of vision-language models. Existing work has largely focused on designing method-specific selecto…
Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation
Shubhankar Borse, Phuc Pham, Farzad Farhadzadeh +6
Despite recent advances in personalized image generation, existing models consistently fail to produce reliable multi-human scenes, often merging or losing facial identity. We pres…
Resolving the Identity Crisis in Text-to-Image Generation
Shubhankar Borse, Farzad Farhadzadeh, Munawar Hayat +1
State-of-the-art text-to-image models suffer from a persistent identity crisis when generating scenes with multiple humans: producing duplicate faces, merging identities, and misco…
MADI: Masking-Augmented Diffusion with Inference-Time Scaling for Visual Editing
Shreya Kadambi, Risheek Garrepalli, Shubhankar Borse +2
Despite the remarkable success of diffusion models in text-to-image generation, their effectiveness in grounded visual editing and compositional control remains challenging. Motiva…
Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
Sunghyun Park, Jungsoo Lee, Shubhankar Borse +4
While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training,…
MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans
Shubhankar Borse, Seokeon Choi, Sunghyun Park +6
Generation of images containing multiple humans, performing complex actions, while preserving their facial identities, is a significant challenge. A major factor contributing to th…
SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization
Shubhankar Borse, Kartikeya Bhardwaj, Mohammad Reza Karimi Dastjerdi +8
Diffusion models are increasingly popular for generative tasks, including personalized composition of subjects and styles. While diffusion models can generate user-specified subjec…
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation
Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse +1
The rising popularity of large foundation models has led to a heightened demand for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), which offer perform…
FouRA: Fourier Low Rank Adaptation
Shubhankar Borse, Shreya Kadambi, Nilesh Prasad Pandey +7
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated imag…
PosSAM: Panoptic Open-vocabulary Segment Anything
Vibashan VS, Shubhankar Borse, Hyojin Park +4
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP…
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Shubhankar Borse, Senthil Yogamani, Marvin Klingner +4
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Shubhankar Borse, Debasmit Das, Hyojin Park +3
We present DejaVu, a novel framework which leverages conditional image regeneration as additional supervision during training to improve deep networks for dense prediction tasks su…
XKD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar +4
Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images…
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
Minghan Zhu, Shizhong Han, Hong Cai +3
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that requires recog…
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Debasmit Das, Shubhankar Borse, Hyojin Park +4
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-…
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar +4
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
Kaifeng Zhang, Yang Fu, Shubhankar Borse +3
While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even…
Learning Implicit Feature Alignment Function for Semantic Segmentation
Hanzhe Hu, Yinbo Chen, Jiarui Xu +4
Integrating high-level context information with low-level details is of central importance in semantic segmentation. Towards this end, most existing segmentation models apply bilin…
Simple and Efficient Architectures for Semantic Segmentation
Dushyant Mehta, Andrii Skliar, Haitam Ben Yahia +4
Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a…
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
Shubhankar Borse, Hyojin Park, Hong Cai +3
This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation. In existing works, it is common to use a shared backbone to extrac…
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
Shubhankar Borse, Hong Cai, Yizhe Zhang +1
While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation…
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
Hong Cai, Janarbek Matai, Shubhankar Borse +3
In this paper, we propose a novel method, X-Distill, to improve the self-supervised training of monocular depth via cross-task knowledge distillation from semantic segmentation to…
Perceptual Consistency in Video Segmentation
Yizhe Zhang, Shubhankar Borse, Hong Cai +4
In this paper, we present a novel perceptual consistency perspective on video semantic segmentation, which can capture both temporal consistency and pixel-wise correctness. Given t…
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation
Yizhe Zhang, Shubhankar Borse, Hong Cai +1
In video segmentation, generating temporally consistent results across frames is as important as achieving frame-wise accuracy. Existing methods rely either on optical flow regular…