10 citations · 32 across the 13 of their papers we have counts for
10 papers · 1 filter
Visual Self-Refinement for Autoregressive Models
Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi +5
Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequen…
X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning
Prasanna Reddy Pulakurthi, Jiamian Wang, Majid Rabbani +3
Prevalent text-to-video retrieval systems mainly adopt embedding models for feature extraction and compute cosine similarities for ranking. However, this design presents two limita…
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation
Prasanna Reddy Pulakurthi, Majid Rabbani, Jamison Heard +3
This work investigates Source-Free Domain Adaptation (SFDA), where a model adapts to a target domain without access to source data. A new augmentation technique, Shuffle PatchMix (…
SHARDeg: A Benchmark for Skeletal Human Action Recognition in Degraded Scenarios
Simon Malzard, Nitish Mital, Richard Walters +3
Computer vision (CV) models for detection, prediction or classification tasks operate on video data-streams that are often degraded in the real world, due to deployment in real-tim…
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
Prasanna Reddy Pulakurthi, Majid Rabbani, Celso M. de Melo +2
This paper introduces a novel dual-region augmentation approach designed to reduce reliance on large-scale labeled datasets while improving model robustness and adaptability across…
Improving Object Detection by Modifying Synthetic Data with Explainable AI
Nitish Mital, Simon Malzard, Richard Walters +3
Limited real-world data severely impacts model performance in many computer vision domains, particularly for samples that are underrepresented in training. Synthetically generated…
AMD: Automatic Multi-step Distillation of Large-scale Vision Models
Cheng Han, Qifan Wang, Sohail A. Dianat +6
Transformer-based architectures have become the de-facto standard models for diverse vision tasks owing to their superior performance. As the size of the models continues to scale…
Prototypical Transformer as Unified Motion Learners
Cheng Han, Yawen Lu, Guohao Sun +9
In this work, we introduce the Prototypical Transformer (ProtoFormer), a general and unified framework that approaches various motion tasks from a prototype perspective. ProtoForme…
Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval
Jiamian Wang, Guohao Sun, Pichao Wang +5
The increasing prevalence of video clips has sparked growing interest in text-video retrieval. Recent advances focus on establishing a joint embedding space for text and video, rel…
Image Translation as Diffusion Visual Programmers
Cheng Han, James C. Liang, Qifan Wang +5
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion model with…