228 research outputs found
Minimizing Dynamic Regret on Geodesic Metric Spaces
In this paper, we consider the sequential decision problem where the goal is
to minimize the general dynamic regret on a complete Riemannian manifold. The
task of offline optimization on such a domain, also known as a geodesic metric
space, has recently received significant attention. The online setting has
received significantly less attention, and it has remained an open question
whether the body of results that hold in the Euclidean setting can be
transplanted into the land of Riemannian manifolds where new challenges (e.g.,
curvature) come into play. In this paper, we show how to get optimistic regret
bound on manifolds with non-positive curvature whenever improper learning is
allowed and propose an array of adaptive no-regret algorithms. To the best of
our knowledge, this is the first work that considers general dynamic regret and
develops "optimistic" online learning algorithms which can be employed on
geodesic metric spaces.Comment: Accepted by Conference on Learning Theory (COLT), 2023. Made some
revisions based on suggestions from anonymous referee
Faster Margin Maximization Rates for Generic Optimization Methods
First-order optimization methods tend to inherently favor certain solutions
over others when minimizing a given training objective with multiple local
optima. This phenomenon, known as implicit bias, plays a critical role in
understanding the generalization capabilities of optimization algorithms.
Recent research has revealed that gradient-descent-based methods exhibit an
implicit bias for the -maximal margin classifier in the context of
separable binary classification. In contrast, generic optimization methods,
such as mirror descent and steepest descent, have been shown to converge to
maximal margin classifiers defined by alternative geometries. However, while
gradient-descent-based algorithms demonstrate fast implicit bias rates, the
implicit bias rates of generic optimization methods have been relatively slow.
To address this limitation, in this paper, we present a series of
state-of-the-art implicit bias rates for mirror descent and steepest descent
algorithms. Our primary technique involves transforming a generic optimization
algorithm into an online learning dynamic that solves a regularized bilinear
game, providing a unified framework for analyzing the implicit bias of various
optimization methods. The accelerated rates are derived leveraging the regret
bounds of online learning algorithms within this game framework
AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism
Generating 3D human motion based on textual descriptions has been a research
focus in recent years. It requires the generated motion to be diverse, natural,
and conform to the textual description. Due to the complex spatio-temporal
nature of human motion and the difficulty in learning the cross-modal
relationship between text and motion, text-driven motion generation is still a
challenging problem. To address these issues, we propose \textbf{AttT2M}, a
two-stage method with multi-perspective attention mechanism: \textbf{body-part
attention} and \textbf{global-local motion-text attention}. The former focuses
on the motion embedding perspective, which means introducing a body-part
spatio-temporal encoder into VQ-VAE to learn a more expressive discrete latent
space. The latter is from the cross-modal perspective, which is used to learn
the sentence-level and word-level motion-text cross-modal relationship. The
text-driven motion is finally generated with a generative transformer.
Extensive experiments conducted on HumanML3D and KIT-ML demonstrate that our
method outperforms the current state-of-the-art works in terms of qualitative
and quantitative evaluation, and achieve fine-grained synthesis and
action2motion. Our code is in https://github.com/ZcyMonkey/AttT2MComment: IEEE International Conference on Computer Vision 2023, 9 page
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
Large language models (LLMs) have been widely used in various applications
but are known to suffer from issues related to untruthfulness and toxicity.
While parameter-efficient modules (PEMs) have demonstrated their effectiveness
in equipping models with new skills, leveraging PEMs for deficiency unlearning
remains underexplored. In this work, we propose a PEMs operation approach,
namely Extraction-before-Subtraction (Ext-Sub), to enhance the truthfulness and
detoxification of LLMs through the integration of ``expert'' PEM and
``anti-expert'' PEM. Remarkably, even anti-expert PEM possess valuable
capabilities due to their proficiency in generating fabricated content, which
necessitates language modeling and logical narrative competence. Rather than
merely negating the parameters, our approach involves extracting and
eliminating solely the deficiency capability within anti-expert PEM while
preserving the general capabilities. To evaluate the effectiveness of our
approach in terms of truthfulness and detoxification, we conduct extensive
experiments on LLMs, encompassing additional abilities such as language
modeling and mathematical reasoning. Our empirical results demonstrate that our
approach effectively improves truthfulness and detoxification, while largely
preserving the fundamental abilities of LLMs
Seeing through events: Real-time moving object sonification for visually impaired people using event-based camera
Scene sonification is a powerful technique to help Visually Impaired People (VIP) understand their surroundings. Existing methods usually perform sonification on the entire images of the surrounding scene acquired by a standard camera or on the priori static obstacles acquired by image processing algorithms on the RGB image of the surrounding scene. However, if all the information in the scene are delivered to VIP simultaneously, it will cause information redundancy. In fact, biological vision is more sensitive to moving objects in the scene than static objects, which is also the original intention of the event-based camera. In this paper, we propose a real-time sonification framework to help VIP understand the moving objects in the scene. First, we capture the events in the scene using an event-based camera and cluster them into multiple moving objects without relying on any prior knowledge. Then, sonification based on MIDI is enabled on these objects synchronously. Finally, we conduct comprehensive experiments on the scene video with sonification audio attended by 20 VIP and 20 Sighted People (SP). The results show that our method allows both participants to clearly distinguish the number, size, motion speed, and motion trajectories of multiple objects. The results show that our method is more comfortable to hear than existing methods in terms of aesthetics
Predicting battery lifetime under varying usage conditions from early aging data
Accurate battery lifetime prediction is important for maintenance, warranties, and cell design. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under varying charge rates, discharge rates, and depths of discharge. The early-life features capture a cell’s state of health and the change rate of component-level degradation modes. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite lithium-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error (MAPE). A hierarchical Bayesian model shows improved performance on extrapolation, achieving 21.8% MAPE for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of battery degradation to inform feature engineering and model construction. Further, a new publicly available battery lifelong aging dataset is provided
Interaction-Driven Active 3D Reconstruction with Object Interiors
We introduce an active 3D reconstruction method which integrates visual
perception, robot-object interaction, and 3D scanning to recover both the
exterior and interior, i.e., unexposed, geometries of a target 3D object.
Unlike other works in active vision which focus on optimizing camera viewpoints
to better investigate the environment, the primary feature of our
reconstruction is an analysis of the interactability of various parts of the
target object and the ensuing part manipulation by a robot to enable scanning
of occluded regions. As a result, an understanding of part articulations of the
target object is obtained on top of complete geometry acquisition. Our method
operates fully automatically by a Fetch robot with built-in RGBD sensors. It
iterates between interaction analysis and interaction-driven reconstruction,
scanning and reconstructing detected moveable parts one at a time, where both
the articulated part detection and mesh reconstruction are carried out by
neural networks. In the final step, all the remaining, non-articulated parts,
including all the interior structures that had been exposed by prior part
manipulations and subsequently scanned, are reconstructed to complete the
acquisition. We demonstrate the performance of our method via qualitative and
quantitative evaluation, ablation studies, comparisons to alternatives, as well
as experiments in a real environment.Comment: Accepted to SIGGRAPH Asia 2023, project page at
https://vcc.tech/research/2023/InterReco
Movie101: A New Movie Understanding Benchmark
To help the visually impaired enjoy movies, automatic movie narrating systems
are expected to narrate accurate, coherent, and role-aware plots when there are
no speaking lines of actors. Existing works benchmark this challenge as a
normal video captioning task via some simplifications, such as removing role
names and evaluating narrations with ngram-based metrics, which makes it
difficult for automatic systems to meet the needs of real application
scenarios. To narrow this gap, we construct a large-scale Chinese movie
benchmark, named Movie101. Closer to real scenarios, the Movie Clip Narrating
(MCN) task in our benchmark asks models to generate role-aware narration
paragraphs for complete movie clips where no actors are speaking. External
knowledge, such as role information and movie genres, is also provided for
better movie understanding. Besides, we propose a new metric called Movie
Narration Score (MNScore) for movie narrating evaluation, which achieves the
best correlation with human evaluation. Our benchmark also supports the
Temporal Narration Grounding (TNG) task to investigate clip localization given
text descriptions. For both two tasks, our proposed methods well leverage
external knowledge and outperform carefully designed baselines. The dataset and
codes are released at https://github.com/yuezih/Movie101.Comment: Accepted to ACL 202
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