Search Results for author:
Found 22 papers, 12 papers with code
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns.
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
Subsequently, we introduce the cross-modal residual distillation to transfer the 3D spatial cues.
A Survey on Large Language Model-Based Game Agents
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI).
Training-Free Semantic Segmentation via LLM-Supervision
Additionally, we propose an assembly that merges the segmentation maps from the various subclass descriptors to ensure a more comprehensive representation of the different aspects in the test images.
Urban Scene Diffusion through Semantic Occupancy Map
Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation.
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain.
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models
We show that this latter insight can be used to enhance the detection of backdoor-poisoned training data.
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar
CenterRadarNet achieves the state-of-the-art result on the K-Radar 3D object detection benchmark.
Causal-DFQ: Causality Guided Data-free Network Quantization
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study
Object tracking is an important functionality of edge video analytic systems and services.
$A^2$Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by language instructions without requiring any path-instruction annotation data.
Riemannian Multinomial Logistics Regression for SPD Neural Networks
Besides, our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks.
Model Sparsity Can Simplify Machine Unlearning
We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient.
Optical Flow Estimation in 360$^\circ$ Videos: Dataset, Model and Application
Moreover, we present a novel Siamese representation Learning framework for Omnidirectional Flow (SLOF) estimation, which is trained in a contrastive manner via a hybrid loss that combines siamese contrastive and optical flow losses.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation
Optical flow estimation in omnidirectional videos faces two significant issues: the lack of benchmark datasets and the challenge of adapting perspective video-based methods to accommodate the omnidirectional nature.
Cross-View Exocentric to Egocentric Video Synthesis
Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view.
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation.
Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network
In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation.
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.
