86 citations · 134 across the 18 of their papers we have counts for
19 papers
Reducing Pretraining-Generation Mismatch in Diffusion Language Models
Xiaocheng Lu, Huabin Liu, Song Guo +1
Diffusion language models (dLLMs) generate text through iterative denoising, allowing multiple tokens to be predicted in parallel. However, pretraining may mask tokens throughout a…
LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models
Fengqi Zhu, Shaoxuan Xu, Jingyang Ou +11
Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understoo…
LLaDA2.1: Speeding Up Text Diffusion via Token Editing
Tiwei Bie, Maosong Cao, Xiang Cao +47
While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generati…
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Tiwei Bie, Maosong Cao, Kun Chen +28
This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (A…
Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning
Zhihao He, Tianyao He, Yun Xu +5
Despite the prosperity of the video language model, the current pursuit of comprehensive video reasoning is thwarted by the inherent spatio-temporal incompleteness within individua…
CogStream: Context-guided Streaming Video Question Answering
Zicheng Zhao, Kangyu Wang, Shijie Li +3
Despite advancements in Video Large Language Models (Vid-LLMs) improving multimodal understanding, challenges persist in streaming video reasoning due to its reliance on contextual…
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning
Tieyuan Chen, Huabin Liu, Yi Wang +8
Video Question Answering (VideoQA) aims to answer natural language questions based on the given video, with prior work primarily focusing on identifying the duration of relevant se…
CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning
Tieyuan Chen, Huabin Liu, Chern Hong Lim +4
Continual learning aims to acquire new knowledge while retaining past information. Class-incremental learning (CIL) presents a challenging scenario where classes are introduced seq…
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Tieyuan Chen, Huabin Liu, Yi Wang +5
Video causal reasoning aims to achieve a high-level understanding of videos from a causal perspective. However, it exhibits limitations in its scope, primarily executed in a questi…
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning
Huabin Liu, Filip Ilievski, Cees G. M. Snoek
This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-langua…
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
Tieyuan Chen, Huabin Liu, Tianyao He +8
Video causal reasoning aims to achieve a high-level understanding of video content from a causal perspective. However, current video reasoning tasks are limited in scope, primarily…
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement
Hao Wu, Huabin Liu, Yu Qiao +1
We present Dive Into the BoundarieS (DIBS), a novel pretraining framework for dense video captioning (DVC), that elaborates on improving the quality of the generated event captions…
Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis
Tianyao He, Huabin Liu, Yuxi Li +4
Video Correlation Learning (VCL), which aims to analyze the relationships between videos, has been widely studied and applied in various general video tasks. However, applying VCL…
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization
Huabin Liu, Weiyao Lin, Tieyuan Chen +3
Current few-shot action recognition involves two primary sources of information for classification:(1) intra-video information, determined by frame content within a single video cl…
Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations
Yufeng Zhang, Weiyao Lin, Wenrui Dai +2
The scene graph is a new data structure describing objects and their pairwise relationship within image scenes. As the size of scene graph in vision applications grows, how to loss…
Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition
Huabin Liu, Weixian Lv, John See +1
A primary challenge faced in few-shot action recognition is inadequate video data for training. To address this issue, current methods in this field mainly focus on devising algori…
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
Rui Qian, Yuxi Li, Huabin Liu +5
The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics…
TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition
Shuyuan Li, Huabin Liu, Rui Qian +5
Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradig…
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
Weiyao Lin, Huabin Liu, Shizhan Liu +7
Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A comple…