6 citations · 16 across the 13 of their papers we have counts for
13 papers
EgoCITE: Context-Augmented Indexing and Time-Aware Retrieval for Long-Horizon Egocentric Memory
Le Zhang, Hao Chen, Vlad Roznyatovskiy +2
Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences. We demonstrate two bottlenecks in existing systems:…
How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning
Qian Yang, Ankur Sikarwar, Huy Le +4
Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they reason in language and discard the fine-grained geometry the task requires. Thinking with i…
DynFrame: Adaptive Reasoning-Driven Multimodal Framework with Dynamic Frame Augmentation for Complex Video Understanding
Peng Zhang, Guanghao Zhang, Wanggui He +10
Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing models to revisit relevant video…
RiT: Vanilla Diffusion Transformers Suffice in Representation Space
Le Zhang, Ning Mang, Aishwarya Agrawal
Flow matching with -prediction -- regressing the clean data point rather than the ambient velocity -- is known to exploit low-dimensional manifold structure effectively in pixel…
DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition
Le Zhang, Shengming Zhang, Rui Zha +3
Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR) systems suffer from error…
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang, Qian Yang, Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do not directly translate to how the…
VisMin: Visual Minimal-Change Understanding
Rabiul Awal, Saba Ahmadi, Le Zhang +1
Fine-grained understanding of objects, attributes, and relationships between objects is crucial for visual-language models (VLMs). Existing benchmarks primarily focus on evaluating…
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
Le Zhang, Yihong Wu, Fengran Mo +2
Multi-modal open-domain question answering typically requires evidence retrieval from databases across diverse modalities, such as images, tables, passages, etc. Even Large Languag…
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
Le Zhang, Jiayang Chen, Tao Shen +2
The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As…
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
Rabiul Awal, Le Zhang, Aishwarya Agrawal
In this paper, we explore effective prompting techniques to enhance zero- and few-shot Visual Question Answering (VQA) performance in contemporary Vision-Language Models (VLMs). Ce…
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
Le Zhang, Rabiul Awal, Aishwarya Agrawal
Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classifica…
SUBS: Subtree Substitution for Compositional Semantic Parsing
Jingfeng Yang, Le Zhang, Diyi Yang
Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Severa…
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Le Zhang, Zichao Yang, Diyi Yang
Data augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word…