45 citations · 63 across the 10 of their papers we have counts for
12 papers
Edit, Don't Imitate: Minimal-Edit On-Policy Distillation for Multimodal Reasoning
Wen Ye, Ailin Deng, Zirui Wang +8
Visual mathematical reasoning can fail before reasoning begins: when a vision-language model (VLM) misreads a diagram, subsequent reasoning proceeds from false premises. This perce…
Scaling Agent Learning via Experience Synthesis
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15
While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts,…
UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework
Tarun Kalluri, Sreyas Ravichandran, Manmohan Chandraker
In this work, we take a deeper look into the diverse factors that influence the efficacy of modern unsupervised domain adaptation (UDA) methods using a large-scale, controlled empi…
Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data
Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn +4
We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from a…
Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos
Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker
We introduce LaGTran, a novel framework that utilizes text supervision to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain…
GeoNet: Benchmarking Unsupervised Adaptation across Geographies
Tarun Kalluri, Wangdong Xu, Manmohan Chandraker
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem per…
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Tarun Kalluri, Weiyao Wang, Heng Wang +3
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy. However, when deployed in the open w…
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
Tarun Kalluri, Manmohan Chandraker
Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source…
MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation
Tarun Kalluri, Astuti Sharma, Manmohan Chandraker
Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approache…
Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation
Astuti Sharma, Tarun Kalluri, Manmohan Chandraker
Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or…
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
Tarun Kalluri, Deepak Pathak, Manmohan Chandraker +1
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the…
Universal Semi-Supervised Semantic Segmentation
Tarun Kalluri, Girish Varma, Manmohan Chandraker +1
In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limit…