Search Results for author:
Found 26 papers, 10 papers with code
Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding
In this method, we design a Cross-Modal Value-Enhanced Decoding(CMVED) module to alleviate hallucination by a novel contrastive decoding mechanism.
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos.
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization
To tackle these issues, we introduce OnlineVPO, a more efficient preference learning approach tailored specifically for video diffusion models.
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training
Experiments demonstrate the effectiveness of our approach, showcasing its state-of-the-art performance on the nuScenes and OpenScene benchmarks for 4D occupancy forecasting, end-to-end motion planning and point cloud forecasting.
Minimax-optimal trust-aware multi-armed bandits
Multi-armed bandit (MAB) algorithms have achieved significant success in sequential decision-making applications, under the premise that humans perfectly implement the recommended policy.
EAGLE: Towards Efficient Arbitrary Referring Visual Prompts Comprehension for Multimodal Large Language Models
To effectively instruct an MLLM, in addition to conventional language expressions, the practice of referring to objects by painting with brushes on images has emerged as a prevalent tool (referred to as "referring visual prompts") due to its efficacy in aligning the user's intention with specific image regions.
EventHallusion: Diagnosing Event Hallucinations in Video LLMs
To mitigate this gap, we propose EventHallusion, a novel benchmark that focuses on assessing the VideoLLMs' hallucination toward event, the crux of video analysis.
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i. e., robustness) and accuracy on natural images (i. e., accuracy).
Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection
Specifically, we devise three modules: Background Category-specific Prompt, Background Object Discovery, and Inference Probability Rectification, to empower the detector to discover, represent, and leverage implicit object knowledge explored from background proposals.
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis
Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation.
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
Latent diffusion models (LDM) have revolutionized text-to-image generation, leading to the proliferation of various advanced models and diverse downstream applications.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
Generating Visual Scenes from Touch
An emerging line of work has sought to generate plausible imagery from touch.
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
On time-consistent equilibrium stopping under aggregation of diverse discount rates
When the sufficient condition of the attitude function is violated, we can illustrate by various examples that the characterization of the optimal equilibrium may differ significantly from some existing results for an individual agent.
A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition
In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN).
Touch and Go: Learning from Human-Collected Vision and Touch
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
Text-to-Table: A New Way of Information Extraction
We first employ a seq2seq model fine-tuned from a pre-trained language model to perform the task.
Modeling Voting for System Combination in Machine Translation
System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.
Neural Machine Translation with Explicit Phrase Alignment
The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints.
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge.
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
THUMT: An Open Source Toolkit for Neural Machine Translation
This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University.
