Toward Realistic Classifier for Long-Tail Distributions

Machine learning models, despite their widespread use in everyday applications, often suffer from unreliable performance due to the distribution shifts between training and inference. Distribution shifts are ubiquitous, occurring in both low-level features and high-level semantics, exacerbated by the non-uniformity of real-world data, particularly in long-tailed distributions where some classes appear much more frequently than others. This imbalance results in non-uniform model performance across classes, posing risks for applications requiring precise information. In contrast, humans are adept at adapting to such challenges. Inspired by this, we focus on addressing the distribution shifts in vision tasks caused by long-tail distributions to make machine learning classifiers more realistic like humans.In this thesis, we aim to redefine long-tail recognition more broadly and concentrate on crafting a classifier that mirrors human adaptability to distribution shifts, a crucial aspect lacking in modern classifiers that is essential for constructing reliable AI systems. Expanding beyond the traditional framework, we extend long-tail recognition to encompass combinatorial label spaces. Furthermore, we explore a hierarchical label space within a single long-tail distribution, offering adaptable control for user-defined systems based on the model's competent level or the desired label space of the user. By delving into the core of the long-tail concept, we demonstrate that significant performance enhancements are attainable through appropriate data sampling techniques, even with straightforward architectures. We also identify hierarchical consistency as a key factor for building a model aligned with human cognition

ProTeCt: Prompt Tuning for Hierarchical Consistency

Large visual-language models, like CLIP, learn generalized representations and have shown promising zero-shot performance. Few-shot adaptation methods, based on prompt tuning, have also been shown to further improve performance on downstream datasets. However, these models are not hierarchically consistent. Frequently, they infer incorrect labels at coarser taxonomic class levels, even when the inference at the leaf level (original class labels) is correct. This is problematic, given their support for open set classification and, in particular, open-grained classification, where practitioners define label sets at various levels of granularity. To address this problem, we propose a prompt tuning technique to calibrate the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency, the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA), are first proposed to benchmark model performance in the open-granularity setting. A prompt tuning technique, denoted as Prompt Tuning for Hierarchical Consistency (ProTeCt), is then proposed to calibrate classification across all possible label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve open-granularity classification performance without degradation of the original classification performance at the leaf level

Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Single-Stage Visual Relationship Learning using Conditional Queries

Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationships. While showing promising results, the pipeline structure induces large parameter and computation overhead, and typically hinders end-to-end optimizations. To address this, recent research attempts to train single-stage models that are computationally efficient. With the advent of DETR, a set based detection model, one-stage models attempt to predict a set of subject-predicate-object triplets directly in a single shot. However, SGG is inherently a multi-task learning problem that requires modeling entity and predicate distributions simultaneously. In this paper, we propose Transformers with conditional queries for SGG, namely, TraCQ with a new formulation for SGG that avoids the multi-task learning problem and the combinatorial entity pair distribution. We employ a DETR-based encoder-decoder design and leverage conditional queries to significantly reduce the entity label space as well, which leads to 20% fewer parameters compared to state-of-the-art single-stage models. Experimental results show that TraCQ not only outperforms existing single-stage scene graph generation methods, it also beats many state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable of end-to-end training and faster inference.Comment: Accepted to NeurIPS 202

4β-Hydroxywithanolide E from Physalis peruviana (golden berry) inhibits growth of human lung cancer cells through DNA damage, apoptosis and G2/M arrest

<p>Abstract</p> <p>Background</p> <p>The crude extract of the fruit bearing plant, <it>Physalis peruviana </it>(golden berry), demonstrated anti-hepatoma and anti-inflammatory activities. However, the cellular mechanism involved in this process is still unknown.</p> <p>Methods</p> <p>Herein, we isolated the main pure compound, 4β-Hydroxywithanolide (4βHWE) derived from golden berries, and investigated its antiproliferative effect on a human lung cancer cell line (H1299) using survival, cell cycle, and apoptosis analyses. An alkaline comet-nuclear extract (NE) assay was used to evaluate the DNA damage due to the drug.</p> <p>Results</p> <p>It was shown that DNA damage was significantly induced by 1, 5, and 10 μg/mL 4βHWE for 2 h in a dose-dependent manner (<it>p </it>< 0.005). A trypan blue exclusion assay showed that the proliferation of cells was inhibited by 4βHWE in both dose- and time-dependent manners (<it>p </it>< 0.05 and 0.001 for 24 and 48 h, respectively). The half maximal inhibitory concentrations (IC₅₀) of 4βHWE in H1299 cells for 24 and 48 h were 0.6 and 0.71 μg/mL, respectively, suggesting it could be a potential therapeutic agent against lung cancer. In a flow cytometric analysis, 4βHWE produced cell cycle perturbation in the form of sub-G₁accumulation and slight arrest at the G₂/M phase with 1 μg/mL for 12 and 24 h, respectively. Using flow cytometric and annexin V/propidium iodide immunofluorescence double-staining techniques, these phenomena were proven to be apoptosis and complete G₂/M arrest for H1299 cells treated with 5 μg/mL for 24 h.</p> <p>Conclusions</p> <p>In this study, we demonstrated that golden berry-derived 4βHWE is a potential DNA-damaging and chemotherapeutic agent against lung cancer.</p