GenAI4ELab/papercli-papers huggingface dataset:
Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
Authors: Haoye Dong; Aviral Chharia; Wenbo Gou; Francisco Vicente Carrasco; Fernando De la Torre
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: 3D Hand reconstruction from a single RGB image is challenging due to the articulated motion, self-occlusion, and interaction with objects. Existing SOTA methods employ attention-based transformers to learn the 3D hand pose and shape, yet they do not fully achieve robust and accurate performance, primarily due to inefficiently modeling spatial relations between joints. To address this problem, we propose a novel graph-guided Mamba framework, named Hamba, which bridges graph learning and state space modeling. Our core idea is to reformulate Mamba's scanning into graph-guided bidirectional scanning for 3D reconstruction using a few effective tokens. This enables us to efficiently learn the spatial relationships between joints for improving reconstruction performance. Specifically, we design a Graph-guided State Space (GSS) block that learns the graph-structured relations and spatial sequences of joints and uses 88.5\% fewer tokens than attention-based methods. Additionally, we integrate the state space features and the global features using a fusion module. By utilizing the GSS block and the fusion module, Hamba effectively leverages the graph-guided state space features and jointly considers global and local features to improve performance. Experiments on several benchmarks and in-the-wild tests demonstrate that Hamba significantly outperforms existing SOTAs, achieving the PA-MPVPE of 5.3mm and F@15mm of 0.992 on FreiHAND. At the time of this paper's acceptance, Hamba holds the top position, Rank 1, in two competition leaderboards on 3D hand reconstruction.
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Authors: Jiahua Dong; Wenqi Liang; Hongliu Li; Duzhen Zhang; Meng Cao; Henghui Ding; Salman Khan; Fahad Khan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably assume that personalized concepts are fixed and cannot change over time. Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts when continually learning a series of new concepts. To address these challenges, we propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM), which can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Specifically, to surmount the catastrophic forgetting of old concepts, we develop a concept consolidation loss and an elastic weight aggregation module. They can explore task-specific and task-shared knowledge during training, and aggregate all low-rank weights of old concepts based on their contributions during inference. Moreover, in order to address concept neglect, we devise a context-controllable synthesis strategy that leverages expressive region features and noise estimation to control the contexts of generated images according to user conditions. Experiments validate that our CIDM surpasses existing custom diffusion models. The source codes are available at https://github.com/JiahuaDong/CIFC.
Tell What You Hear From What You See - Video to Audio Generation Through Text
Authors: Xiulong Liu; Kun Su; Eli Shlizerman
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The content of visual and audio scenes is multi-faceted such that a video stream can
be paired with various audio streams and vice-versa. Thereby, in video-to-audio
generation task, it is imperative to introduce steering approaches for controlling the
generated audio. While Video-to-Audio generation is a well-established generative
task, existing methods lack such controllability. In this work, we propose VATT, a
multi-modal generative framework that takes a video and an optional text prompt
as input, and generates audio and optional textual description (caption) of the
audio. Such a framework has two unique advantages: i) Video-to-Audio generation
process can be refined and controlled via text which complements the context
of the visual information, and ii) The model can suggest what audio to generate
for the video by generating audio captions. VATT consists of two key modules:
VATT Converter, which is an LLM that has been fine-tuned for instructions and
includes a projection layer that maps video features to the LLM vector space, and
VATT Audio, a bi-directional transformer that generates audio tokens from visual
frames and from optional text prompt using iterative parallel decoding. The audio
tokens and the text prompt are used by a pretrained neural codec to convert them
into a waveform. Our experiments show that when VATT is compared to existing
video-to-audio generation methods in objective metrics, such as VGGSound audiovisual dataset, it achieves competitive performance when the audio caption is
not provided. When the audio caption is provided as a prompt, VATT achieves
even more refined performance (with lowest KLD score of 1.41). Furthermore,
subjective studies asking participants to choose the most compatible generated
audio for a given silent video, show that VATT Audio has been chosen on average
as a preferred generated audio than the audio generated by existing methods. VATT
enables controllable video-to-audio generation through text as well as suggesting
text prompts for videos through audio captions, unlocking novel applications such
as text-guided video-to-audio generation and video-to-audio captioning.
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
Authors: Dongzhi Jiang; Guanglu Song; Xiaoshi Wu; Renrui Zhang; Dazhong Shen; Zhuofan Zong; Yu Liu; Hongsheng Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. We break down the problem into two causes: concept ignorance and concept mismapping. To tackle the two challenges, we propose CoMat, an end-to-end diffusion model fine-tuning strategy with the image-to-text concept matching mechanism. Firstly, we introduce a novel image-to-text concept activation module to guide the diffusion model in revisiting ignored concepts. Additionally, an attribute concentration module is proposed to map the text conditions of each entity to its corresponding image area correctly. Extensive experimental evaluations, conducted across three distinct text-to-image alignment benchmarks, demonstrate the superior efficacy of our proposed method, CoMat-SDXL, over the baseline model, SDXL~\cite{podell2023sdxl}. We also show that our method enhances general condition utilization capability and generalizes to the long and complex prompt despite not specifically training on it.
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
Authors: KeZheng Xiong; Haoen Xiang; Qingshan Xu; Chenglu Wen; Siqi Shen; Jonathan Li; Cheng Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud registration methods have recently emerged to circumvent the need for costly pose annotations. However, they fail to establish reliable optimization objectives for unsupervised training, either relying on overly strong geometric assumptions, or suffering from poor-quality pseudo-labels due to inadequate integration of low-level geometric and high-level contextual information. We have observed that in the feature space, latent new inlier correspondences tend to cluster
around respective positive anchors that summarize features of existing inliers. Motivated by this observation, we propose a novel unsupervised registration method termed INTEGER to incorporate high-level contextual information for reliable pseudo-label mining. Specifically, we propose the Feature-Geometry Coherence Mining module to dynamically adapt the teacher for each mini-batch of data during training and discover reliable pseudo-labels by considering both high-level feature representations and low-level geometric cues. Furthermore, we propose Anchor-Based Contrastive Learning to facilitate contrastive learning with anchors for a robust feature space. Lastly, we introduce a Mixed-Density Student to learn density-invariant features, addressing challenges related to density variation and low overlap in the outdoor scenario. Extensive experiments on KITTI and nuScenes datasets demonstrate that our INTEGER achieves competitive performance in terms of accuracy and generalizability.
Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions
Authors: Wenjing Yan; Xuanyu Cao
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Distribution variations in machine learning, driven by the dynamic nature of deployment environments, significantly impact the performance of learning models. This paper explores endogenous distribution shifts in learning systems, where deployed models influence environments and subsequently alter data distributions. This phenomenon is formulated by a decision-dependent distribution mapping within the recently proposed framework of performative prediction (PP) Perdomo et al. (2020). We investigate the performative effect in a decentralized noncooperative game, where players aim to minimize private cost functions while simultaneously managing coupled inequality constraints. Under performativity, we examine two equilibrium concepts for the studied game: performative stable equilibrium (PSE) and Nash equilibrium (NE), and establish sufficient conditions for their existence and uniqueness. Notably, we provide the first upper bound on the distance between the PSE and NE in the literature, which is challenging to evaluate due to the absence of strong convexity on the joint cost function. Furthermore, we develop a decentralized stochastic primal-dual algorithm for efficiently computing the PSE point. By carefully bounding the performative effect in theoretical analysis, we prove that the proposed algorithm achieves sublinear convergence rates for both performative regrets and constraint violation and maintains the same order of convergence rate as the case without performativity. Numerical experiments validate the effectiveness of our algorithm and theoretical results.
Persistent Test-time Adaptation in Recurring Testing Scenarios
Authors: Trung-Hieu Hoang; MinhDuc Vo; Minh N. Do
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Current test-time adaptation (TTA) approaches aim to adapt a machine learning model to environments that change continuously. Yet, it is unclear whether TTA methods can maintain their adaptability over prolonged periods. To answer this question, we introduce a diagnostic setting - **recurring TTA** where environments not only change but also recur over time, creating an extensive data stream. This setting allows us to examine the error accumulation of TTA models, in the most basic scenario, when they are regularly exposed to previous testing environments. Furthermore, we simulate a TTA process on a simple yet representative $\epsilon$-**perturbed Gaussian Mixture Model Classifier**, deriving theoretical insights into the dataset- and algorithm-dependent factors contributing to gradual performance degradation. Our investigation leads us to propose **persistent TTA (PeTTA)**, which senses when the model is diverging towards collapse and adjusts the adaptation strategy, striking a balance between the dual objectives of adaptation and model collapse prevention. The supreme stability of PeTTA over existing approaches, in the face of lifelong TTA scenarios, has been demonstrated over comprehensive experiments on various benchmarks. Our project page is available at [https://hthieu166.github.io/petta](https://hthieu166.github.io/petta).
SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition
Authors: Feng Lu; Xinyao Zhang; Canming Ye; Shuting Dong; Lijun Zhang; Xiangyuan Lan; Chun Yuan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Visual place recognition (VPR) is an essential task for multiple applications such as augmented reality and robot localization. Over the past decade, mainstream methods in the VPR area have been to use feature representation based on global aggregation, as exemplified by NetVLAD. These features are suitable for large-scale VPR and robust against viewpoint changes. However, the VLAD-based aggregation methods usually learn a large number of (e.g., 64) clusters and their corresponding cluster centers, which directly leads to a high dimension of the yielded global features. More importantly, when there is a domain gap between the data in training and inference, the cluster centers determined on the training set are usually improper for inference, resulting in a performance drop. To this end, we first attempt to improve NetVLAD by removing the cluster center and setting only a small number of (e.g., only 4) clusters. The proposed method not only simplifies NetVLAD but also enhances the generalizability across different domains. We name this method SuperVLAD. In addition, by introducing ghost clusters that will not be retained in the final output, we further propose a very low-dimensional 1-Cluster VLAD descriptor, which has the same dimension as the output of GeM pooling but performs notably better. Experimental results suggest that, when paired with a transformer-based backbone, our SuperVLAD shows better domain generalization performance than NetVLAD with significantly fewer parameters. The proposed method also surpasses state-of-the-art methods with lower feature dimensions on several benchmark datasets. The code is available at https://github.com/lu-feng/SuperVLAD.
Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk
Authors: Yuzhou Gu; Nikki Lijing Kuang; Yian Ma; Zhao Song; Lichen Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We consider the problem of sampling from a $d$-dimensional log-concave distribution $\pi(\theta) \propto \exp(-f(\theta))$ for $L$-Lipschitz $f$, constrained to a convex body (described by $n$ hyperplanes) equipped with a barrier function, contained in a ball of radius $R$ with a $w$-warm start. 

We propose a \emph{robust} sampling framework that computes spectral approximations to the Hessian of the barrier functions in each iteration. We prove that for the polytope constraints, sampling with the Lee-Sidford barrier function mixes within $\widetilde O((d^2+dL^2R^2)\log(w/\delta))$ steps with a per step cost of $\widetilde O(nd^{\omega-1})$, where $\omega\approx 2.37$ is the fast matrix multiplication exponent. Compared to the prior work of Mangoubi and Vishnoi, our approach gives faster mixing time as we are able to design a generalized soft-threshold Dikin walk beyond log-barrier.

We further extend our result to show how to sample from a $d$-dimensional spectrahedron, the constrained set of a semidefinite program, specified by the set $\{x\in \mathbb{R}^d: \sum_{i=1}^d x_i A_i \succeq C \}$ where $A_1,\ldots,A_d, C$ are $n\times n$ real symmetric matrices. We design a walk that mixes in $\widetilde O((nd+dL^2R^2)\log(w/\delta))$ steps with a per iteration cost of $\widetilde O(n^\omega+n^2d^{3\omega-5})$. We improve the mixing time bound of prior best Dikin walk due to Narayanan and Rakhlin that mixes in $\widetilde O((n^2d^3+n^2dL^2R^2)\log(w/\delta))$ steps.
MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
Authors: Takeshi Noda; Chao Chen; Weiqi Zhang; Xinhai Liu; Yu-Shen Liu; Zhizhong Han
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Latest methods employ supervised learning or pretrained priors to learn a signed distance function (SDF). However, neural networks tend to smooth local details due to the lack of ground truth signed distnaces or normals, which limits the performance of learning-based methods in reconstruction tasks. To resolve this issue, we propose a novel method, named MultiPull, to learn multi-scale implicit fields from raw point clouds to optimize accurate SDFs from coarse to fine. We achieve this by mapping 3D query points into a set of frequency features, which makes it possible to leverage multi-level features during optimization. Meanwhile, we introduce optimization constraints from the perspective of spatial distance and normal consistency, which play a key role in point cloud reconstruction based on multi-scale optimization strategies. Our experiments on widely used object and scene benchmarks demonstrate that our method outperforms the state-of-the-art methods in surface reconstruction.
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation
Authors: Jiaming Lv; Haoyuan Yang; Peihua Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been  predominant, and recently its variants have achieved compelling performance.  However, KL-Div  only compares probabilities of the corresponding category between the teacher and student while lacking a mechanism for cross-category comparison.  Besides, KL-Div is problematic when applied to intermediate layers, as it cannot handle non-overlapping distributions and is unaware of geometry of the underlying manifold. To address these downsides, we propose a methodology of Wasserstein Distance (WD) based knowledge distillation. Specifically, we propose  a logit distillation method called WKD-L based on discrete WD, which performs cross-category comparison of  probabilities and thus can explicitly leverage  rich interrelations among categories.  Moreover, we introduce a feature distillation method called WKD-F, which  uses a parametric method for modeling feature distributions and adopts continuous WD  for transferring knowledge from  intermediate layers. Comprehensive evaluations on image classification and object detection  have shown  (1) for logit distillation WKD-L outperforms very strong KL-Div variants; (2) for feature distillation WKD-F is superior to the KL-Div counterparts and state-of-the-art competitors.
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Authors: Yimeng Zhang; Xin Chen; Jinghan Jia; Yihua Zhang; Chongyu Fan; Jiancheng Liu; Mingyi Hong; Ke Ding; Sijia Liu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but they also pose safety risks, such as the potential generation of harmful content and copyright violations. The techniques of machine unlearning, also known as concept erasing, have been developed to address these risks. However, these techniques remain vulnerable to adversarial prompt attacks, which can prompt DMs post-unlearning to regenerate undesired images containing concepts (such as nudity) meant to be erased. This work aims to enhance the robustness of concept erasing by integrating the principle of adversarial training (AT) into machine unlearning, resulting in the robust unlearning framework referred to as AdvUnlearn. However, achieving this effectively and efficiently is highly nontrivial. First, we find that a straightforward implementation of AT compromises DMs’ image generation quality post-unlearning. To address this, we develop a utility-retaining regularization on an additional retain set, optimizing the trade-off between concept erasure robustness and model utility in AdvUnlearn. Moreover, we identify the text encoder as a more suitable module for robustification compared to UNet, ensuring unlearning effectiveness. And the acquired text encoder can serve as a plug-and-play robust unlearner for various DM types. Empirically, we perform extensive experiments to demonstrate the robustness advantage of AdvUnlearn across various DM unlearning scenarios, including the erasure of nudity, objects, and style concepts. In addition to robustness, AdvUnlearn also achieves a balanced tradeoff with model utility. To our knowledge, this is the first work to systematically explore robust DM unlearning through AT, setting it apart from existing methods that overlook robustness in concept erasing. Codes are available at https://github.com/OPTML-Group/AdvUnlearn.

Warning: This paper contains model outputs that may be offensive in nature.
Learnability of high-dimensional targets by two-parameter models and gradient flow
Authors: Dmitry Yarotsky
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We explore the theoretical possibility of learning $d$-dimensional targets with $W$-parameter models by gradient flow (GF) when $W<d$. Our main result shows that if the targets are described by a particular $d$-dimensional probability distribution, then there exist models with as few as two parameters that can learn the targets with arbitrarily high success probability. On the other hand, we show that for $W<d$ there is necessarily a large subset of GF-non-learnable targets. In particular, the set of learnable targets is not dense in $\mathbb R^d$, and any subset of $\mathbb R^d$ homeomorphic to the $W$-dimensional sphere contains non-learnable targets. Finally, we observe that the model in our main theorem on almost guaranteed two-parameter learning is constructed using a hierarchical procedure and as a result is not expressible by a single elementary function. We show that this limitation is essential in the sense that most models written in terms of elementary functions cannot achieve the learnability demonstrated in this theorem.
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
Authors: Trong-Thuan Nguyen; Pha Nguyen; Xin Li; Jackson Cothren; Alper Yilmaz; Khoa Luu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Video scene graph generation (VidSGG) has emerged as a transformative approach to capturing and interpreting the intricate relationships among objects and their temporal dynamics in video sequences. In this paper, we introduce the new AeroEye dataset that focuses on multi-object relationship modeling in aerial videos. Our AeroEye dataset features various drone scenes and includes a visually comprehensive and precise collection of predicates that capture the intricate relationships and spatial arrangements among objects. To this end, we propose the novel Cyclic Graph Transformer (CYCLO) approach that allows the model to capture both direct and long-range temporal dependencies by continuously updating the history of interactions in a circular manner. The proposed approach also allows one to handle sequences with inherent cyclical patterns and process object relationships in the correct sequential order. Therefore, it can effectively capture periodic and overlapping relationships while minimizing information loss. The extensive experiments on the AeroEye dataset demonstrate the effectiveness of the proposed CYCLO model, demonstrating its potential to perform scene understanding on drone videos. Finally, the CYCLO method consistently achieves State-of-the-Art (SOTA) results on two in-the-wild scene graph generation benchmarks, i.e., PVSG and ASPIRe.