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Found 15 papers, 1 papers with code
Calibrating Long-form Generations from Large Language Models
Addressing this gap, we introduce a unified calibration framework, in which both the correctness of the LLMs' responses and their associated confidence levels are treated as distributions across a range of scores.
DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views.
Pragmatic Evaluation of Clarifying Questions with Fact-Level Masking
Here we present a definition and framework for natural language pragmatic asking of clarifying questions (PACQ), the problem of generating questions that result in answers useful for a reasoning task.
TOSS:High-quality Text-guided Novel View Synthesis from a Single Image
In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image.
DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation
Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation.
Generalized UAV Object Detection via Frequency Domain Disentanglement
This loss enables the network to concentrate on extracting domain-invariant spectrum and domain-specific spectrum, so as to achieve better disentangling results.
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning
Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications.
Representation Learning
Video-Based Person Re-Identification
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models
We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models.
Neural Dependencies Emerging from Learning Massive Categories
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
Rank Diminishing in Deep Neural Networks
By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i. e., ResNets, deep MLPs, and Transformers on ImageNet.
Learning a Better Initialization for Soft Prompts via Meta-Learning
So pre-trained prompt tuning (PPT) is proposed to initialize prompts by leveraging pre-training data.
ICDM 2020 Knowledge Graph Contest: Consumer Event-Cause Extraction
To this end, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel sequence tagging framework, instead of extracting event types and events-causes separately.
Attack-Guided Perceptual Data Generation for Real-World Re-Identification
In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e. g., cross-resolution and insufficient lighting.
Real-world Person Re-Identification via Degradation Invariance Learning
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e. g., low-resolution, weak illumination, blurring and adverse weather.
