133 citations · 266 across the 30 of their papers we have counts for
31 papers
DeGRe: Dense-supervised Generative Reranking for Recommendation
Chaotian Song, Jingyao Zhang, Chenghao Chen +6
In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequenc…
RePO: Bridging On-Policy Learning and Off-Policy Knowledge through Rephrasing Policy Optimization
Linxuan Xia, Xiaolong Yang, Yongyuan Chen +4
Aligning large language models (LLMs) on domain-specific data remains a fundamental challenge. Supervised fine-tuning (SFT) offers a straightforward way to inject domain knowledge…
Any-to-Bokeh: Arbitrary-Subject Video Refocusing with Video Diffusion Model
Yang Yang, Siming Zheng, Qirui Yang +6
Diffusion models have recently emerged as powerful tools for camera simulation, enabling both geometric transformations and realistic optical effects. Among these, image-based boke…
SUDO: Enhancing Text-to-Image Diffusion Models with Self-Supervised Direct Preference Optimization
Liang Peng, Boxi Wu, Haoran Cheng +2
Previous text-to-image diffusion models typically employ supervised fine-tuning (SFT) to enhance pre-trained base models. However, this approach primarily minimizes the loss of mea…
Discriminator-Free Direct Preference Optimization for Video Diffusion
Haoran Cheng, Qide Dong, Liang Peng +7
Direct Preference Optimization (DPO), which aligns models with human preferences through win/lose data pairs, has achieved remarkable success in language and image generation. Howe…
GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D Generators
Hengjia Li, Yang Liu, Yibo Zhao +9
Recently, 3D generative domain adaptation has emerged to adapt the pre-trained generator to other domains without collecting massive datasets and camera pose distributions. Typical…
PersonalVideo: High ID-Fidelity Video Customization without Dynamic and Semantic Degradation
Hengjia Li, Haonan Qiu, Shiwei Zhang +6
The current text-to-video (T2V) generation has made significant progress in synthesizing realistic general videos, but it is still under-explored in identity-specific human video g…
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
Xiaopei Wu, Yuenan Hou, Xiaoshui Huang +8
Training deep models for LiDAR semantic segmentation is challenging due to the inherent sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity p…
Searching Priors Makes Text-to-Video Synthesis Better
Haoran Cheng, Liang Peng, Linxuan Xia +5
Significant advancements in video diffusion models have brought substantial progress to the field of text-to-video (T2V) synthesis. However, existing T2V synthesis model struggle t…
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Zhanwei Zhang, Minghao Chen, Shuai Xiao +7
Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels,…
LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models
Yang Yang, Wen Wang, Liang Peng +8
Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging ta…
UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation
Hengjia Li, Yang Liu, Yuqi Lin +8
Recently, generative domain adaptation has achieved remarkable progress, enabling us to adapt a pre-trained generator to a new target domain. However, existing methods simply adapt…
Local Conditional Controlling for Text-to-Image Diffusion Models
Yibo Zhao, Liang Peng, Yang Yang +9
Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the genera…
SmoothVideo: Smooth Video Synthesis with Noise Constraints on Diffusion Models for One-shot Video Tuning
Liang Peng, Haoran Cheng, Zheng Yang +6
Recent one-shot video tuning methods, which fine-tune the network on a specific video based on pre-trained text-to-image models (e.g., Stable Diffusion), are popular in the communi…
NormKD: Normalized Logits for Knowledge Distillation
Zhihao Chi, Tu Zheng, Hengjia Li +4
Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potenti…
Learning Occupancy for Monocular 3D Object Detection
Liang Peng, Junkai Xu, Haoran Cheng +6
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Hengjia Li, Tu Zheng, Zhihao Chi +5
Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention
Wenxiao Wang, Wei Chen, Qibo Qiu +5
While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first pro…
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
Yuqi Lin, Minghao Chen, Wenxiao Wang +5
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training cos…
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
Minghao Chen, Renbo Tu, Chenxi Huang +3
Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly…
GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds
Honghui Yang, Tong He, Jiaheng Liu +5
Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks such as image and video, exploring MAE in large-scale 3D point clouds remains challenging du…
One-shot Implicit Animatable Avatars with Model-based Priors
Yangyi Huang, Hongwei Yi, Weiyang Liu +6
Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large…
Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz +31
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang +4
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization…