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Wenhui Zhu, Xiwen Chen, Peijie Qiu, Aristeidis Sotiras, Abolfazl Razi, Yalin Wang

Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting lesions. However, existing MIL mainstream methods focus on modeling correlation between instances while overlooking the inherent diversity among instances. Meanwhile, few MIL methods aimed at diversity modeling have emerged, showing a performance gap with mainstream MIL methods and facing diversity limits due to computational constraints. To bridge this gap, we propose a novel MIL aggregation method based on diverse global representation (DGR-MIL), by modeling diversity among instances through a set of global vectors. As a result, the global vectors serve as a summary of all instances. First, we turn the instance correlation into the similarity between instance embeddings and the predefined global vectors through a cross-attention mechanism. This stems from the fact that similar instance embeddings typically would result in a higher correlation with a certain global vector. Second, we propose two mechanisms to enforce the diversity among the global vectors to be more descriptive of the entire bag: (i) positive instance alignment and (ii) a novel, efficient, and theoretically guaranteed diversification learning. Specifically, the positive instance alignment module encourages the global vectors to align with instances of interest center (e.g., tumor WSI/bag). To further diversify the global representations, we propose a novel diversity loss. The proposed model outperforms the state-of-the-art MIL aggregation models by a substantial margin on the CAMELYON-16 and the TCGA-lung cancer datasets. The source code will be released upon acceptance.

Rohan Choudhury, Koichiro Niinuma, Kris Kitani, Laszlo Jeni

We propose to answer questions about videos by generating short procedural programs that solve visual subtasks to obtain a final answer. We present Procedural Video Querying (ProViQ) which uses a large language model to generate such programs from an input question and an API of visual modules in the prompt, then executes them to obtain the output. Recent similar procedural approaches have proven successful for image question answering, but cannot effectively or efficiently answer questions about videos due to their image-centric modules and lack of temporal reasoning ability. We address this by providing ProViQ with novel modules intended for video understanding, allowing it to generalize to a wide variety of videos with no additional training. As a result, ProViQ can efficiently find relevant moments in long videos, do causal and temporal reasoning, and summarize videos over long time horizons in order to answer complex questions. This code generation framework additionally enables ProViQ to perform other video tasks beyond question answering, such as multi-object tracking or basic video editing. ProViQ achieves state-of-the-art results on a diverse range of benchmarks, with improvements of up to 25% on short, long, open-ended, multiple-choice and multimodal video question-answering datasets. We include video demos and code in the supplement.

Kaile Du, Yifan Zhou, Fan Lyu, Yuyang Li, Chen Lu, Guangcan Liu

The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions, exacerbating catastrophic forgetting within the disjoint label space. In this paper, we aim to refine multi-label confidence calibration in MLCIL and propose a Confidence Self-Calibration (CSC) approach. Firstly, for label relationship calibration, we introduce a class-incremental graph convolutional network that bridges the isolated label spaces by constructing learnable, dynamically extended label relationship graph. Then, for confidence calibration, we present a max-entropy regularization for each multi-label increment, facilitating confidence self-calibration through the penalization of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology.

Yao-Chih Lee, Zhoutong Zhang, Kevin Blackburn-Matzen, Simon Niklaus, Jianming Zhang, Jia-Bin Huang, Feng Liu

Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

Junfei Xiao, Ziqi Zhou, Wenxuan Li, Shiyi Lan, Jieru Mei, Zhiding Yu, Bingchen Zhao, Alan Yuille, Yuyin Zhou, Cihang Xie

This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models. It is based on two core designs. First, we employ Large Language Models (LLMs) and carefully crafted prompts to generate descriptions of all involved categories that carry meaningful common sense knowledge and follow a structured format. Second, we introduce a description embedding model preserving semantic correlation across descriptions and then cluster them into a set of descriptive properties (e.g., 256) using K-Means. These properties are based on interpretable common sense knowledge consistent with theories of human recognition. We empirically show that our approach makes segmentation models perform stronger on five classic benchmarks (e.g., ADE20K, COCO-Stuff, Pascal Context, Cityscapes, and BDD). Our method also shows better scalability with extended training steps than category-level supervision. Our interpretable segmentation framework also emerges with the generalization ability to segment out-of-domain or unknown categories using only in-domain descriptive properties. Codes will be publicly available.

Wenke Huang, Mang Ye, zekun shi, Bo Du, Dacheng Tao

Federated learning presents massive potential for privacy-friendly vision task collaboration. However, the federated visual performance is deeply affected by backdoor attacks, where malicious clients optimize on triggered samples to mislead the global model into targeted mispredictions. Existing backdoor defensive solutions are normally based on two assumptions: data homogeneity and minority malicious ratio for the elaborate client-wise defensive rules. To address existing limitations, we argue that heterogeneous clients and backdoor attackers both bring divergent optimization directions and thus it is hard to discriminate them precisely. In this paper, we argue that parameters appear in different important degrees towards distinct distribution and instead consider meaningful and meaningless parameters for the ideal target distribution. We propose the Self-Driven Fisher Calibration (SDFC), which utilizes the Fisher Information to calculate the parameter importance degree for the local agnostic and global validation distribution and regulate those elements with large important differences. Furthermore, we allocate high aggregation weight for clients with relatively small overall parameter differences, which encourages clients with close local distribution to the global distribution, to contribute more to the federation. This endows SDFC to handle backdoor attackers in heterogeneous federated learning. Various vision task performances demonstrate the effectiveness of SDFC.

Yang Liu, Chuanchen Luo, Lue Fan, Naiyan Wang, Junran Peng, Zhaoxiang Zhang

The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Specifically, the global scene prior and adaptive training data selection enables efficient training and seamless fusion. Based on fused Gaussian primitives, we generate different detail levels through compression, and realize fast rendering across various scales through the proposed block-wise detail levels selection and aggregation strategy. Extensive experimental results on large-scale scenes demonstrate that our approach attains state-of-the-art rendering quality, enabling consistent real-time rendering of large-scale scenes across vastly different scales.

Anh Thai, Weiyao Wang, Hao Tang, Stefan Stojanov, James Rehg, Matt Feiszli

3D object part segmentation is essential in computer vision applications. While substantial progress has been made in 2D object part segmentation, the 3D counterpart has received less attention, in part due to the scarcity of annotated 3D datasets, which are expensive to collect. In this work, we propose to leverage a few annotated 3D shapes or richly annotated 2D datasets to perform 3D object part segmentation. We present our novel approach, termed 3-By-2 that achieves SOTA performance on different benchmarks with various granularity levels. By using features from pretrained foundation models and exploiting semantic and geometric correspondences, we are able to overcome the challenges of limited 3D annotations. Our approach leverages available 2D labels, enabling effective 3D object part segmentation. Our method 3-By-2 can accommodate various part taxonomies and granularities, demonstrating interesting part label transfer ability across different object categories. We will release code to the community.

Yulin Luo, Ruichuan An, Bocheng Zou, Yiming Tang, Jiaming Liu, Shanghang Zhang

Subpopulation structure is a set of hierarchical relations among several subpopulations determined by a certain criteria. Discovering such structure provides comprehensive understanding of the dataset, which is benefitial to many downstream tasks, such as subpopulation shifts and slice discovery. Despite important, we find there has been no work that systematically explore the subpopulation structure of datasets. Considering that solving this task requires the method to have a broad understanding of various aspects of the datasets, in this work, we leverage the world knowledge, summarization, and instruction-following capabilities of Large Language Model (LLM) to explore the latent subpopulation structure of image datasets. Specifically, we propose a novel approach named Subpopulation Structure Discovery with Large Language Models (SSD-LLM), whose core idea is to generate and analyze the informative image captions and then summarize the structure characteristic of datasets based on the analysis using LLM. SSD-LLM consists of two novel prompt engineering components, Criteria Initialization and Criteria Self-Refinement, which ensures an token-efficient and reliable discovery process. SSD-LLM offers a unified paradigm to address multiple downstream tasks with simple task-specific prompt tuning, including dataset organization, longt tail attribute identification, slice discovery and our proposed slice prediction. We validate the effectiveness of SSD-LLM through these subpopulation-related tasks. We hope to inspire the community to explore potential of LLM as dataset analyst.

Hritam Basak, Zhaozheng Yin

Semi-supervised Domain Adaptation (SSDA) encompasses the process of adapting representations acquired from the source domain to a new target domain, utilizing a limited number of labeled samples in conjunction with an abundance of unlabeled data from the target domain. Simple aggregation of domain adaptation (DA) and semi-supervised learning (SSL) falls short of optimal performance due to two primary challenges: (1) skewed training data distribution favoring the source representation learning, and (2) the persistence of superfluous domain-specific features, hindering effective domain-agnostic (i.e., task-specific) feature extraction. In pursuit of greater generalizability and robustness, we present an SSDA framework with a new episodic learning strategy: \lq\lq learn, forget, then learn more\rq\rq. First, we train two encoder-classifier pairs, one for the source and the other for the target domain, aiming to learn domain-specific features. This involves minimizing classification loss for in-domain images and maximizing uncertainty loss for out-of-domain images. Subsequently, we transform the images into a new space, strategically unlearning (forgetting) the domain-specific representations while preserving their structural similarity to the originals. This proactive removal of domain-specific attributes is complemented by learning more domain-agnostic features using a Gaussian-guided latent alignment (GLA) strategy that uses a prior distribution to align domain-agnostic source and target representations. The proposed SSDA framework can be further extended to unsupervised domain adaptation (UDA). Evaluation across {two} domain adaptive image classification tasks reveals our method's superiority over state-of-the-art (SoTA) methods in both SSDA and UDA scenarios. Code will be released.

Ashkan Mirzaei, Tristan T Aumentado-Armstrong, Marcus A Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos Derpanis, Igor Gilitschenski

The success of denoising diffusion models in generating and editing images has sparked interest in using diffusion models for editing 3D scenes represented via neural radiance fields (NeRFs). However, current 3D editing methods lack a way to both pinpoint the edit location and limit changes to the desired volumetric region. Consequently, these methods often over-edit, altering irrelevant parts of the scene. We introduce a new task, 3D edit localization, to automatically identify the relevant region for an editing task and restrict the edit accordingly. To achieve this goal, we initially tackle 2D edit localization, and then lift it to multiple views to address the 3D localization challenge. For 2D localization, we leverage InstructPix2Pix (IP2P) and identify the discrepancy between IP2P predictions with and without the instruction. We refer to this discrepancy as the relevance map. The relevance map conveys the importance of changing each pixel to achieve an edit, and guides downstream modifications, ensuring that pixels irrelevant to the edit remain unchanged. With the relevance maps of multiview posed images, we can define the \textit{relevance field}, defining the 3D region within which modifications should be made. This enables us to improve the quality of text-guided 3D NeRF scene editing, by performing iterative updates on the training views, guided by renders from the relevance field. Our method achieves state-of-the-art performance on both NeRF and image editing tasks. We will make the code available.

Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal Patel

Large diffusion-based Text-to-Image (T2I) has shown impressive generative powers for text-to-image generation and spatially conditioned generation which makes the generated image follow the semantics of the condition. For most applications, we can train the model end-to-end with paired data to obtain photorealistic generation quality. However to add an additional task, one often needs to retrain the model from scratch using paired data across all modalities to retrain good generation performance. In this paper, we tackle this issue and propose a novel strategy to scale a generative model across new tasks with minimal compute. During our experiments, we discovered that the variance maps of intermediate feature maps of diffusion models capture the intensity of conditioning. utilizing this prior information, we propose MaxFusion, an efficient strategy to scale up text-to-image generation models. Specifically, we combine aligned features of multiple models hence bringing a compositional effect. Our fusion strategy can be integrated into off-the-shelf models to enhance their generative prowess. We show the effectiveness of our method by utilizing off-the-shelf models for multi-modal generation to show the effectiveness of our method. To facilitate further research, we will make the code public.

Yuzhong Zhao, Liu Yue, Zonghao Guo, Weijia Wu, Chen Gong, Qixiang Ye, Fang Wan

Region-level captioning is challenged by the caption degeneration issue, which refers to that pre-trained multimodal models tend to predict the most frequent captions but miss the less frequent ones. In this study, we propose a controllable region-level captioning (ControlCap) approach, which introduces control words to a multimodal model to address the caption degeneration issue. In specific, ControlCap leverages a discriminative module to generate control words within the caption space to partition it to multiple sub-spaces. The multimodal model is constrained to generate captions within a few sub-spaces containing the control words, which increases the opportunity of hitting less frequent captions, alleviating the caption degeneration issue. Furthermore, interactive control words can be given by either a human or an expert model, which enables captioning beyond the training caption space, enhancing the model’s generalization ability. Extensive experiments on Visual Genome and RefCOCOg datasets show that ControlCap respectively improves the CIDEr score by 21.6 and 2.2, outperforming the state-of-the-arts by significant margins. The code is enclosed in the supplementary material

Minh Tran, Di Chang, Maksim Siniukov, Mohammad Soleymani

Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.

Luc Sträter, Mohammadreza Salehi, Efstratios Gavves, Cees Snoek, Yuki M Asano

In the domain of anomaly detection, methods often excel in either semantic or industrial benchmarks, rarely achieving cross-domain proficiency. In this paper, we present GeneralAD, an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings with minimal per-task adjustments. In our approach, we capitalize on the inherent design of Vision Transformers, which are trained on image patches, thereby ensuring that the last hidden states retain a patch-based structure. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features to construct pseudo-abnormal samples. These features are fed to an attention-based discriminator, which is trained to score every patch in the image. With this, our method can both accurately identify anomalies at the image level and also generate interpretable anomaly maps. We extensively evaluated our approach on 10 benchmarks, achieving state-of-the-art results in 6 datasets and on-par performance in the remaining for both localization and detection tasks.

Dewen Zeng, Xinrong Hu, Yawen Wu, Xiaowei Xu, Yiyu Shi

Contrastive learning with the nearest neighbor has proved to be one of the most efficient self-supervised learning (SSL) techniques by utilizing the similarity of multiple instances within the same class. However, its efficacy is constrained as the nearest neighbor algorithm primarily identifies "easy" positive pairs, where the representations are already closely located in the embedding space. In this paper, we introduce a novel approach called Contrastive Learning with Synthetic Positives (CLSP) that utilizes synthetic images, generated by an unconditional diffusion model, as the additional positives to help the model learn from diverse positives. Through feature interpolation in the diffusion model sampling process, we generate images with distinct backgrounds yet similar semantic content to the anchor image. These images are considered "hard" positives for the anchor image, and when included as supplementary positives in the contrastive loss, they contribute to a performance improvement of over 2% and 1% in linear evaluation compared to the previous NNCLR and All4One methods across multiple benchmark datasets such as CIFAR10, achieving state-of-the-art methods. On transfer learning benchmarks, CLSP outperforms existing SSL frameworks on 6 out of 8 downstream datasets. We believe CLSP establishes a valuable baseline for future SSL studies incorporating synthetic data in the training process.

Farzad Farhadzadeh, Qiqi Hou, Hoang Le, Amir Said, Randall R Rauwendaal, Alex Bourd, Fatih Porikli

Advances in rendering have led to tremendous growth in texture assets, including resolution, complexity, and novel textures components, but this growth in data volume has not been matched by advances in its compression. Meanwhile Neural Image Compression (NIC) has advanced significantly and shown promising results, but the proposed methods cannot be directly adapted to neural texture compression. First, texture compression requires on-demand and real-time decoding with random access during parallel rendering (e.g. block texture decompression on GPUs). Additionally, NIC does not support multi-resolution reconstruction (mip-levels), nor does it have the ability to efficiently jointly compress different sets of texture channels. In this work, we introduce a novel approach to texture set compression that integrates traditional GPU texture representation and NIC techniques, designed to enable random access and support many-channel texture sets. To achieve this goal, we propose an asymmetric auto-encoder framework that employs a convolutional encoder to capture detailed information in a bottleneck-latent space, and at decoder side we utilize a fully connected network, whose inputs are sampled latent features plus positional information, for a given texture coordinate and mip level. This latent data is defined to enable simplified access to multi-resolution data by simply changing the scanning strides. Experimental results demonstrate that this approach provides much better results than conventional texture compression, and significant improvement over the latest method using neural networks.

Amir Bar, Arya Bakhtiar, Danny L Tran, Antonio Loquercio, Jathushan Rajasegaran, yann lecun, Amir Globerson, Trevor Darrell

Animals perceive the world to plan their actions and interact with other agents to accomplish complex tasks, demonstrating capabilities that are still unmatched by AI systems. To advance our understanding and reduce the gap between the capabilities of animals and AI systems, we introduce a dataset of pet egomotion imagery with diverse examples of simultaneous egomotion and multi-agent interaction. Current video datasets separately contain egomotion and interaction examples, but rarely both at the same time. In addition, EgoPet offers a radically distinct perspective from existing egocentric datasets of humans or vehicles. We define two in-domain benchmark tasks that capture animal behavior, and a third benchmark to assess the utility of EgoPet as a pretraining resource to robotic quadruped locomotion, showing that models trained from EgoPet outperform those trained from prior datasets. This work provides evidence that today's pets could be a valuable resource for training future AI systems and robotic assistants.

Haoning Wu, Hanwei Zhu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Annan Wang, Wenxiu Sun, Qiong Yan 等

Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses. In this work, we extend the edge of emerging large multi-modality models (LMMs) to further advance visual quality comparison into open-ended settings, that 1) can respond to open-range questions on quality comparison; 2) can provide detailed reasonings beyond direct answers. To this end, we propose the Co-Instruct. To train this first-of-its-kind open-source open-ended visual quality comparer, we collect the Co-Instruct-562K dataset, from two sources: (a) LLM-merged single image quality description, (b) GPT-4V “teacher” responses on unlabeled data. Furthermore, to better evaluate this setting, we propose the MICBench, the first benchmark on multi-image comparison for LMMs. We demonstrate that Co-Instruct not-only achieves in average 30% higher accuracy than state-of-the-art open-source LMMs, but also outperforms GPT-4V (its teacher), on both existing quality-related benchmarks and the proposed MICBench. We will publish our datasets, training scripts and model weights upon acceptance.

Ming-Feng Li, Yueh-Feng Ku, Hong-Xuan Yen, Chi Liu, Yu-Lun Liu, Albert Y Chen, Cheng-Hao Kuo, Min Sun

Sparse RGBD scene completion is a challenging task especially when considering consistent textures and geometries throughout the entire scene. Different from existing solutions that rely on human-designed text prompts or predefined camera trajectories, we propose GenRC, an automated training-free pipeline to complete a room-scale 3D mesh with high-fidelity textures. To achieve this, we first project the sparse RGBD images to a highly incomplete 3D mesh. Instead of iteratively generating novel views to fill in the void, we utilized our proposed E-Diffusion to generate a view-consistent panoramic RGBD image which ensures global geometry and appearance consistency. Furthermore, we maintain the input-output scene stylistic consistency through textual inversion to replace human-designed text prompts. To bridge the domain gap among datasets, E-Diffusion leverages models trained on large-scale datasets to generate diverse appearances. GenRC outperforms state-of-the-art methods under most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories.