104 citations · 147 across the 43 of their papers we have counts for
45 papers
Many-Shot CoT-ICL: Making In-Context Learning Truly Learn
Tsz Ting Chung, Lemao Liu, Mo Yu +1
While many-shot ICL achieves remarkable performance, prior studies of its scaling behavior have mainly focused on non-reasoning tasks. In this work, we study many-shot ICL on reaso…
FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation
Chenhan Jiang, Yu Chen, Qingwen Zhang +4
The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse and precise camera trajector…
STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting
Shi Quan Foo, Chi-Ho Wong, Zhihan Gao +3
Precipitation nowcasting is a critical spatio-temporal prediction task for society to prevent severe damage owing to extreme weather events. Despite the advances in this field, the…
DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
Tsz Ting Chung, Lemao Liu, Mo Yu +1
Logic reasoning in natural language has been recognized as an important measure of human intelligence for Large Language Models (LLMs). Popular benchmarks may entangle multiple rea…
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Junjie Wu, Gefei Gu, Yanan Zheng +2
Long-context language models (LCLMs) have exhibited impressive capabilities in long-context understanding tasks. Among these, long-context referencing -- a crucial task that requir…
SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension
Junjie Wu, Jiangnan Li, Yuqing Li +6
Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to de…
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Kai Chen, Ruiyuan Gao, Lanqing Hong +11
In this paper, we present details of the 1st W-CODA workshop, held in conjunction with the ECCV 2024. W-CODA aims to explore next-generation solutions for autonomous driving corner…
Learning 3D Persistent Embodied World Models
Siyuan Zhou, Yilun Du, Yuncong Yang +4
The ability to simulate the effects of future actions on the world is a crucial ability of intelligent embodied agents, enabling agents to anticipate the effects of their actions a…
CoherenDream: Boosting Holistic Text Coherence in 3D Generation via Multimodal Large Language Models Feedback
Chenhan Jiang, Yihan Zeng, Dit-Yan Yeung
Score Distillation Sampling (SDS) has achieved remarkable success in text-to-3D content generation. However, SDS-based methods struggle to maintain semantic fidelity for user promp…
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting
Chiu-Wai Yan, Shi Quan Foo, Van Hoan Trinh +3
Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-…
Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models
Junjie Wu, Tsz Ting Chung, Kai Chen +1
Despite the outstanding performance in vision-language reasoning, Large Vision-Language Models (LVLMs) might generate hallucinated contents that do not exist in the given image. Mo…
AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models
Jiaming Zhang, Junhong Ye, Xingjun Ma +5
Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios. However, recent studies have revealed that V…
EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
Kai Chen, Yunhao Gou, Runhui Huang +28
GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language…
Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection
Zhili Chen, Shuangjie Xu, Maosheng Ye +4
The Bird's-Eye-View (BEV) representation is a critical factor that directly impacts the 3D object detection performance, but the traditional BEV grid representation induces quadrat…
JointDreamer: Ensuring Geometry Consistency and Text Congruence in Text-to-3D Generation via Joint Score Distillation
Chenhan Jiang, Yihan Zeng, Tianyang Hu +4
Score Distillation Sampling (SDS) by well-trained 2D diffusion models has shown great promise in text-to-3D generation. However, this paradigm distills view-agnostic 2D image distr…
Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation
Meng Qin, Chaorui Zhang, Yu Gao +2
Community detection (CD) is a classic graph inference task that partitions nodes of a graph into densely connected groups. While many CD methods have been proposed with either impr…
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Kai Chen, Yanze Li, Wenhua Zhang +10
Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted…
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Yibo Wang, Ruiyuan Gao, Kai Chen +8
Current perceptive models heavily depend on resource-intensive datasets, prompting the need for innovative solutions. Leveraging recent advances in diffusion models, synthetic data…
TransformMix: Learning Transformation and Mixing Strategies from Data
Tsz-Him Cheung, Dit-Yan Yeung
Data augmentation improves the generalization power of deep learning models by synthesizing more training samples. Sample-mixing is a popular data augmentation approach that create…
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
Yunhao Gou, Kai Chen, Zhili Liu +6
Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Althoug…
IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding
Meng Qin, Dit-Yan Yeung
Network embedding, which maps graphs to distributed representations, is a unified framework for various graph inference tasks. According to the topology properties (e.g., structura…
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
Yunhao Gou, Zhili Liu, Kai Chen +6
Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vi…
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
Pengxiang Li, Kai Chen, Zhili Liu +7
Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a…
Gaussian Shell Maps for Efficient 3D Human Generation
Rameen Abdal, Wang Yifan, Zifan Shi +6
Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks…