1 citations · 2 across the 6 of their papers we have counts for
8 papers
Scaling Multilingual Semantic Search in Uber Eats Delivery
Bo Ling, Zheng Liu, Haoyang Chen +3
We present a production-oriented semantic retrieval system for Uber Eats that unifies retrieval across stores, dishes, and grocery/retail items. Our approach fine-tunes a Qwen2 two…
Task-Aware KV Compression For Cost-Effective Long Video Understanding
Minghao Qin, Yan Shu, Peitian Zhang +6
Long-video understanding (LVU) remains a severe challenge for existing multimodal large language models (MLLMs), primarily due to the prohibitive computational cost. Recent approac…
Video-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification
Minghao Qin, Xiangrui Liu, Zhengyang Liang +6
Multi-modal large language models (MLLMs) models have made significant progress in video understanding over the past few years. However, processing long video inputs remains a majo…
VideoExplorer: Think With Videos For Agentic Long-Video Understanding
Huaying Yuan, Zheng Liu, Junjie Zhou +5
Long-video understanding~(LVU) is a challenging problem in computer vision. Existing methods either downsample frames for single-pass reasoning, sacrificing fine-grained details, o…
Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval
Ze Liu, Zhengyang Liang, Junjie Zhou +2
With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms. In this work, we formally define an emerging IR paradigm…
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…