u/RoofProper328

Running into the same wall on a couple of industrial inspection projects and curious how other people have dealt with it.

The line runs well, which is the problem. Out of a few hundred thousand parts we've got maybe 200 real defects, and they're spread across six or seven types, so some classes have under 20 examples. Classic supervised segmentation just doesn't have anything to learn from.

Options as I see them:

Anomaly detection on good samples only. PaDiM, PatchCore, that family. Works, but it flags anything unusual including a smudge on the lens or a part sitting at a weird angle, and the false positive rate on a real line has been rough.

Synthetic defects. Painting cracks and scratches onto good images. Ours look obviously fake next to real ones and I suspect the model is learning "was this pasted" rather than "is this damaged."

Buy or scrape more defect data. But defects are extremely specific to the part and the process. A scratch on someone else's aluminium housing doesn't look like a scratch on ours.

Just wait and collect. Realistic answer, but that's 18 months and the project needs to justify itself sooner.

What I'm actually unsure about is whether the 20-example classes are even worth modelling separately, or whether it's smarter to collapse everything into a binary defect/no-defect call and let a human sort the type afterwards. Losing the classification hurts the reporting side but it might be the only honest thing to do with that little data.

Anyone shipped something in this situation? Especially interested if you went anomaly detection and got the false positives down to something a QA team would tolerate.

Older ASR writeups all list the same challenge set: accents and dialects, context, background noise, code-switching, and visual cues for video. Most of that was written when the pipeline was still acoustic model plus pronunciation lexicon plus language model.

End-to-end changed the architecture completely, but I'm not sure it changed the failure modes as much as people claim. My read:

Context — mostly solved. Attention over the full utterance handles what n-gram LMs couldn't.

Noise — better, not solved. Large-scale pretraining bought a lot of robustness, but it's still SNR-dependent and degrades in ways that feel arbitrary.

Accents — this one I think got worse in a specific sense. WER dropped for everyone, but the gap between well-represented and underrepresented accents didn't close proportionally. It just became less visible because the average number looks good.

Code-switching — barely moved. Still mostly a data problem, and the multilingual models tend to lock onto one language per utterance.

Visual cues — basically abandoned outside of research. AV-ASR papers exist, nobody deploys it.

Curious whether people running production systems agree. Specifically: is accent robustness actually an architecture problem at this point, or is it purely training distribution? And has anyone gotten code-switching to work without building a dedicated dataset for the specific language pair?

Most collected call-center corpora are recorded in some form of controlled setup: scripted or semi-scripted scenarios, participants playing agent and customer, clean capture. Real production audio is 8kHz narrowband through a codec, with overlapping speech, hold music bleed, background noise on the customer side, and genuinely frustrated prosody that nobody acts convincingly.

So :-

1. If you fine-tune on collected/simulated call data, do you actually see WER improvement on real traffic, or does it mostly help on the collected test split and flatten out in production?
2. Does codec simulation (downsampling, G.711/Opus round-trip, packet loss) close enough of the gap to matter, or is it cosmetic?
3. Code-switching is where I'm least sure. Hinglish, Taglish, Spanglish — real agents switch mid-sentence constantly and collected data underrepresents it badly. Anyone found a way to elicit natural code-switching without it going stiff?
4. Where's the actual ceiling — is it acoustics, or is it diarization and overlapping turns?

Not looking for vendor recs, just want to know whether the acted-vs-real gap is as big in practice as I suspect.

Most collected call-center corpora are recorded in some form of controlled setup: scripted or semi-scripted scenarios, participants playing agent and customer, clean capture. Real production audio is 8kHz narrowband through a codec, with overlapping speech, hold music bleed, background noise on the customer side, and genuinely frustrated prosody that nobody acts convincingly.

So :-

1. If you fine-tune on collected/simulated call data, do you actually see WER improvement on real traffic, or does it mostly help on the collected test split and flatten out in production?
2. Does codec simulation (downsampling, G.711/Opus round-trip, packet loss) close enough of the gap to matter, or is it cosmetic?
3. Code-switching is where I'm least sure. Hinglish, Taglish, Spanglish — real agents switch mid-sentence constantly and collected data underrepresents it badly. Anyone found a way to elicit natural code-switching without it going stiff?
4. Where's the actual ceiling — is it acoustics, or is it diarization and overlapping turns?

Not looking for vendor recs, just want to know whether the acted-vs-real gap is as big in practice as I suspect.

Facial recognition keeps improving on paper, but the data side feels like a mess to me. A lot of the well-known datasets were scraped without consent, and bias across skin tones, age, and lighting conditions is still a real problem.

For those working on FR systems: how are you sourcing training data that's both diverse enough to avoid bias and actually collected with consent? Are you licensing from vendors, collecting your own, or relying on public datasets and hoping for the best? Curious where people draw the line ethically vs practically.

I was looking through a public dataset yesterday and realized something.

Some images felt like they could reasonably have two different labels depending on who's annotating them.

Do companies just write really detailed guidelines, or is there another process for keeping everyone consistent?

I'm curious how this works in practice because it seems like even small inconsistencies could affect the model later.

Over the last year, I've started to think that we spend a lot of time talking about models, GPUs, and benchmarks, but not nearly enough time talking about the data behind them.

A model can only learn from what it's shown. If the labels are inconsistent, edge cases are ignored, or the data doesn't reflect real-world conditions, even a strong model will struggle once it's deployed.

What's interesting is that many companies now seem to spend just as much effort defining annotation guidelines, reviewing disagreements, and improving label quality as they do training the models themselves.

It feels like we're reaching a point where better data pipelines might create more value than simply making models larger.

Curious if others working in ML have seen the same trend, or if you think model architecture is still the bigger differentiator.

I've been trying a few AI voice assistants recently, and one thing I've noticed is that they usually perform well when I speak clearly.

The moment I interrupt myself, hesitate, switch languages, or someone else starts talking nearby, the experience gets noticeably worse.

It made me wonder whether the biggest limitation today is actually the models or whether most systems simply aren't trained on enough real-world conversations.

Would love to hear from anyone building speech or conversational AI systems.

I've noticed that more robotics projects seem to be talking about first-person (egocentric) video instead of regular camera footage.

At first I assumed video is just video, but now I'm wondering if the viewpoint actually makes a big difference.

For example, a first-person view naturally captures things like:

• where the hands are moving
• how objects are being manipulated
• what the operator is paying attention to
• the exact sequence of actions

Does that make it significantly better for training robots, or is it mainly useful for specific tasks like manipulation?

Would love to hear from anyone working in robotics or embodied AI. Is first-person data becoming the new standard, or are third-person datasets still enough for most applications?

Been experimenting with a few speech AI demos lately, and one thing I keep noticing is that they work surprisingly well for "standard" speech but can fall off pretty quickly when people switch languages mid-sentence or have strong regional accents.

It made me wonder if this is mostly a model limitation, or if it's actually a training data problem. I imagine collecting enough high-quality multilingual and accent-diverse speech data must be much harder than it sounds.

For people working on ASR or conversational AI, what's currently the bigger challenge:

• model architecture,
• lack of diverse speech datasets,
• or the cost/complexity of collecting and annotating real-world audio?

Curious to hear what people in the field think, especially if you've deployed speech systems in multilingual environments.

Working through a project that needs fused annotation across LiDAR point clouds, camera frames, and radar, and the QA side is turning into the hard part. Single-modality labeling QA is straightforward enough, but once you're checking consistency across sensors — temporal alignment, object IDs matching between point cloud and image, that kind of thing — it gets messy fast.

For people who've done this at scale: are you running multi-pass human review, building automated consistency checks between modalities, or some mix? And how do you keep reviewer fatigue from quietly tanking label quality on the 3D side? Curious what's actually working vs. what sounds good in theory.

I’ve been reading more about healthcare AI recently and it feels like model performance is only one small part of the challenge.

From people actually working in this space, what ends up being the hardest problem in practice?

Is it getting access to quality clinical data, handling privacy/compliance, annotation accuracy, bias across patient populations, or something else entirely?

A lot of papers make progress look fast, but I’m curious what the real-world blockers are when trying to deploy healthcare AI at scale.

I have been looking into face liveness and anti-spoofing solutions recently. I’m curious about how people are dealing with real-world attacks, especially with deepfakes and replay attacks improving significantly.

Many demos perform well against printed photos. However, what is actually effective in production against screen replays, AI-generated faces, masks, and so on?

Are most teams developing custom models in-house or depending on third-party SDKs or APIs for this? I would love to hear practical experiences instead of just benchmark numbers.

Hi everyone,

I registered for CVPR 2026, but I haven’t seen any official mobile app announcement yet for Android/iOS.

Is there any official CVPR 2026 app released or expected soon for schedules, networking, workshops, etc.?

Would appreciate if anyone has details or download links. Thanks!

Something that has been on my mind lately:

Humans can usually get used to a place and learn fast with just a little bit of experience.

For example a person can figure out rooms, objects, obstacles and how things move around after seeing just a few examples.

Physical AI systems seem to need a huge amount of real-world data, simulation, retraining and coverage of all the edge cases before they work well.

Then small changes in the environment can still cause them to fail.

Some examples of these changes include:

• lighting differences
• object placement changes
• sensor drift
• human behavior
• timing variations

Is the main reason for this that current systems still don't really understand space and the world around them?

Do we really need a lot of different kinds of data, for AI systems that interact with the world?

Been reading more about Physical AI lately and one thing that keeps standing out is how different real-world deployment is compared to benchmark testing.

A lot of Vision AI systems seem fine in controlled environments, but once they move into real spaces:

• lighting changes
• occlusion happens constantly
• sensor streams drift
• humans behave unpredictably
• environments evolve over time

performance drops pretty quickly.

It feels like the challenge is becoming less about raw detection accuracy and more about contextual understanding + multimodal data quality.

This article had an interesting breakdown of how Vision AI is being used to help Physical AI systems interpret real-world environments beyond just frame-by-frame detection:

Physical AI: How Vision AI Helps Machines Understand the Real World

Curious whether people here think current Vision AI architectures are enough for long-term Physical AI systems, or if we still need fundamentally different approaches for scene understanding and real-world adaptation.

Been experimenting with a few CV models recently and something keeps bothering me.

A model can look great during testing, but once you put it into actual real-world conditions, performance drops way more than expected.

Stuff like:

• bad lighting
• weird camera angles
• motion blur
• partial visibility
• crowded scenes
• inconsistent annotations

seems to affect results a lot more than model benchmarks suggest.

Starting to wonder if dataset quality/diversity is becoming a bigger problem than the models themselves.

Curious how people here handle this in production systems, especially around edge cases and maintaining high-quality training data over time.

Most of the conversation around Physical AI seem to be around models and reasoning but the harder problem may be gathering enough real world multimodal data (video, motion, sensor data, interactions, edge cases etc.) at scale.

Do people think Physical AI is currently more limited by models or by the difficulty of building high-quality real-world data pipelines out here?